--------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  V:\Health_IT\Urgent_Care\R2\ucc_replication\BR_EntryThreshold_PCSA_F.log
  log type:  text
 opened on:  23 Aug 2023, 11:53:14

. 
. // Descriptives
. 
. use "PCSALevelData_v3.dta", clear

. replace cat_aucc = og_n_hospaffucc_geo
(149 real changes made)

. replace cat_aucc = 2 if cat_aucc>2
(99 real changes made)

. replace n_hospaffucc_geo = cat_aucc
(149 real changes made)

. 
. // Appendix Table 2
. tabstat tot_pop rural income_pc hispanic nonhisp_black gte_highschool age_65 uninsured cms_wage_
> index any_hosp any_aucc, by(cat_ucc) stat(mean sd) columns(stat)

Summary for variables: tot_pop rural income_pc hispanic nonhisp_black gte_highschool age_65 uninsu
> red cms_wage_index any_hosp any_aucc
Group variable: cat_ucc 

 cat_ucc |      Mean        SD
---------+--------------------
       0 |  .0015231  .0013126
         |   .523039  .3844909
         |  2.605765  .5979022
         |  .1444822   .180316
         |  .0774958  .1918935
         |  .4331051  .0581394
         |   .193787  .0624179
         |  .1133529  .0628619
         |  .9583163  .1055214
         |  .8131868  .3904773
         |  .1282051  .3349321
---------+--------------------
       1 |  .0055631  .0048499
         |  .3429664  .2850999
         |  2.592342   .500314
         |  .1458027  .2133667
         |  .0248008  .0593717
         |  .4336779  .0502865
         |  .1796048  .0537383
         |   .108334  .0492042
         |  .9424467  .1464537
         |   .963964  .1872251
         |  .2972973  .4591414
---------+--------------------
       2 |  .0081738   .007384
         |   .286713   .278494
         |  2.780966  .5201162
         |  .1224647   .153168
         |  .0178483  .0437393
         |  .4515853  .0363163
         |  .1679951  .0376648
         |   .088603  .0385347
         |  .9428364  .1343405
         |  .9692308  .1740358
         |  .5076923  .5038315
---------+--------------------
       3 |  .0218554  .0177189
         |  .1258862  .1833632
         |  2.978875  .6864644
         |  .1530375  .1816657
         |  .0080948  .0130056
         |  .4564807  .0440915
         |  .1524165  .0425414
         |  .0954294  .0436285
         |  .9492012  .1626915
         |  .9642857  .1859925
         |    .59375  .4922323
---------+--------------------
   Total |  .0095991  .0139062
         |  .3383266  .3476375
         |  2.744658  .6307886
         |   .145421  .1840357
         |  .0399444  .1292905
         |  .4427647  .0517167
         |  .1751872  .0557399
         |  .1041691   .053431
         |  .9511699  .1362404
         |  .9034175  .2956081
         |  .3476969  .4765934
------------------------------

. // Appendix Table 4
. tabstat tot_pop rural income_pc hispanic nonhisp_black gte_highschool age_65 uninsured cms_wage_
> index any_hosp any_ucc, by(cat_aucc) stat(mean sd) columns(stat)

Summary for variables: tot_pop rural income_pc hispanic nonhisp_black gte_highschool age_65 uninsu
> red cms_wage_index any_hosp any_ucc
Group variable: cat_aucc 

cat_aucc |      Mean        SD
---------+--------------------
       0 |  .0054324  .0083288
         |  .4149123   .374498
         |  2.672001  .6217398
         |  .1422596  .1816945
         |  .0556191  .1575128
         |  .4377067  .0543528
         |  .1842326  .0608213
         |  .1071514  .0563427
         |  .9541424  .1329165
         |  .8519362  .3555684
         |  .4578588  .4987894
---------+--------------------
       1 |  .0110846  .0111308
         |  .2267195   .219657
         |  2.660868  .4670124
         |  .1462718  .2192761
         |  .0114741  .0187524
         |  .4411829  .0466334
         |  .1673951  .0424283
         |  .1040691  .0550292
         |  .9237577  .1198902
         |         1         0
         |  .7529412  .4338609
---------+--------------------
       2 |  .0210282  .0204494
         |  .1763497  .2352058
         |  3.006527  .6709162
         |  .1542503  .1693682
         |  .0100037  .0133543
         |  .4585696  .0428742
         |  .1529818  .0369288
         |  .0954393  .0417853
         |  .9580499  .1527463
         |         1         0
         |  .9060403  .2927567
---------+--------------------
   Total |  .0095991  .0139062
         |  .3383266  .3476375
         |  2.744658  .6307886
         |   .145421  .1840357
         |  .0399444  .1292905
         |  .4427647  .0517167
         |  .1751872  .0557399
         |  .1041691   .053431
         |  .9511699  .1362404
         |  .9034175  .2956081
         |  .5943536  .4913819
------------------------------

. 
. // Programs
. 
. capture program drop brentry

. qui do "brentry_v2.do"

. capture program drop brentry_bioprobit_neg

. qui do "brentry_bioprobit_negbin_v3.do"

. 
. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. putexcel A1 = "2-type"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel B1 = "Low income"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel C1 = "High income"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel D1 = "High SVI"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel E1 = "Low SVI"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel F1 = "High uninsured"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel G1 = "Low uninsured"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel H1 = "1-type"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel I1 = "3-type"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel J1 = "Low income"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel K1 = "High income"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel L1 = "High SVI"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel M1 = "Low SVI"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel N1 = "High uninsured"
file BR_EntryThreshold_Results_V7.xlsx saved

. putexcel O1 = "Low uninsured"
file BR_EntryThreshold_Results_V7.xlsx saved

. 
. ********************************************************************************
. // Ordered probit: no endogeneity
. ********************************************************************************
. 
. constraint drop 

. constraint 1 tot_pop = 1

. ml clear

. ml model lf brentry (ucc_s:cat_ucc = tot_pop, nocons) (ucc_v:cat_ucc = n_hospitals rural income_
> pc hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (ucc_f:cat_ucc = cms_wage_ind
> ex, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3, constraints(1)

. eststo clear

. eststo: ml max, difficult iterate(100) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -1325.2632
Rescale:      Log likelihood = -937.66157
Rescale eq:   Log likelihood = -561.91444
Iteration 0:  Log likelihood = -1116.3446  
Iteration 1:  Log likelihood = -565.07364  
Iteration 2:  Log likelihood = -503.13241  
Iteration 3:  Log likelihood =  -496.2796  
Iteration 4:  Log likelihood = -496.12813  
Iteration 5:  Log likelihood = -496.12772  

                                                           Number of obs = 673
                                                           Wald chi2(0)  =   .
Log likelihood = -496.12772                                Prob > chi2   =   .

 ( 1)  [ucc_s]tot_pop = 1
--------------------------------------------------------------------------------
               | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
ucc_s          |
       tot_pop |          1  (constrained)
---------------+----------------------------------------------------------------
ucc_v          |
   n_hospitals |  -23.61826   14.38337    -1.64   0.101    -51.80915    4.572635
         rural |   52.24716   43.49065     1.20   0.230    -32.99294    137.4873
     income_pc |  -39.86697     11.328    -3.52   0.000    -62.06944   -17.66449
      hispanic |  -69.29385   51.03168    -1.36   0.175    -169.3141    30.72641
 nonhisp_black |  -300.8539   242.2883    -1.24   0.214    -775.7303    174.0225
gte_highschool |    382.773   232.6051     1.65   0.100    -73.12454    838.6705
        age_65 |   308.0181   189.4579     1.63   0.104    -63.31254    679.3488
     uninsured |    93.6999   192.6045     0.49   0.627    -283.7979    471.1977
---------------+----------------------------------------------------------------
ucc_f          |
cms_wage_index |   .2112208   .4556227     0.46   0.643    -.6817833    1.104225
---------------+----------------------------------------------------------------
       /ucc_a1 |   373.4509   104.6353     3.57   0.000     168.3695    578.5322
       /ucc_a2 |   279.4913   37.47824     7.46   0.000     206.0353    352.9473
       /ucc_a3 |    5.35547    10.7591     0.50   0.619    -15.73198    26.44292
       /ucc_g1 |    1.33338   .4527585     2.95   0.003     .4459898    2.220771
       /ucc_g2 |   .0032009   .0988919     0.03   0.974    -.1906236    .1970255
       /ucc_g3 |    .491361   .0994393     4.94   0.000     .2964635    .6862585
--------------------------------------------------------------------------------
(est1 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. qui do "BR_EntryThreshold_og.do" 10000 "H"

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.336581   .4492218     2.98   0.003     .4561226     2.21704
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.827942   .4555038     4.01   0.000     .9351712    2.720713
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    93.9596    98.7807     0.95   0.342    -99.64701    287.5662
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   88.60413   99.61945     0.89   0.374    -106.6464    283.8547
------------------------------------------------------------------------------

. 
. ********************************************************************************
. // Multivariate ordered probit: addressing endogeneity
. ********************************************************************************
. 
. constraint drop

. constraint 1 tot_pop = 1

. constraint 2 tot_pop2 = 1

. ml clear

. ml model lf brentry_bioprobit_neg (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = rural
>  income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (hosp_f:cat_hosp2 = c
> ms_wage_index con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (ucc_v
> :cat_ucc = n_hospitals rural income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, n
> ocons) (ucc_f:cat_ucc = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3 
> /r, constraints(1 2) technique(bfgs)

. eststo: ml max, difficult iterate(200) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -1654.4063
Rescale:      Log likelihood = -1331.3532
Rescale eq:   Log likelihood = -863.05874
Iteration 0:  Log likelihood = -1473.7246  
Iteration 1:  Log likelihood = -1254.2823  (backed up)
Iteration 2:  Log likelihood = -1195.4356  (backed up)
Iteration 3:  Log likelihood = -1193.2004  (backed up)
Iteration 4:  Log likelihood = -1192.8623  (backed up)
Iteration 5:  Log likelihood = -1189.0343  (backed up)
Iteration 6:  Log likelihood = -1185.9734  
Iteration 7:  Log likelihood = -975.73323  
Iteration 8:  Log likelihood = -973.24819  
Iteration 9:  Log likelihood = -933.34234  
Iteration 10: Log likelihood = -925.53321  
Iteration 11: Log likelihood = -923.79616  
Iteration 12: Log likelihood = -921.71662  
Iteration 13: Log likelihood = -920.53149  
Iteration 14: Log likelihood = -916.08891  
Iteration 15: Log likelihood = -909.06976  
Iteration 16: Log likelihood = -909.06972  (backed up)
Iteration 17: Log likelihood = -909.06711  (backed up)
Iteration 18: Log likelihood = -904.21699  
Iteration 19: Log likelihood = -886.87435  
Iteration 20: Log likelihood = -869.97551  
Iteration 21: Log likelihood =  -865.2551  
Iteration 22: Log likelihood = -863.32242  
Iteration 23: Log likelihood = -862.12485  
Iteration 24: Log likelihood = -860.74991  
Iteration 25: Log likelihood =  -856.6284  
Iteration 26: Log likelihood = -849.45862  
Iteration 27: Log likelihood = -845.37995  
Iteration 28: Log likelihood = -843.83787  
Iteration 29: Log likelihood = -841.29083  
Iteration 30: Log likelihood = -829.36287  
Iteration 31: Log likelihood = -813.35841  
Iteration 32: Log likelihood = -810.75422  
Iteration 33: Log likelihood = -810.51487  
Iteration 34: Log likelihood = -810.37172  
Iteration 35: Log likelihood = -810.22675  
Iteration 36: Log likelihood = -809.79252  
Iteration 37: Log likelihood = -807.15141  
Iteration 38: Log likelihood = -802.00116  
Iteration 39: Log likelihood = -801.20022  
Iteration 40: Log likelihood = -801.15954  
Iteration 41: Log likelihood = -801.05555  
Iteration 42: Log likelihood =  -799.8856  
Iteration 43: Log likelihood = -798.57877  
Iteration 44: Log likelihood =   -798.446  
Iteration 45: Log likelihood = -798.44059  
Iteration 46: Log likelihood = -798.42404  
Iteration 47: Log likelihood = -798.04956  
Iteration 48: Log likelihood = -797.79091  
Iteration 49: Log likelihood = -797.76791  
Iteration 50: Log likelihood = -797.75874  
Iteration 51: Log likelihood = -797.46423  
Iteration 52: Log likelihood = -796.63725  
Iteration 53: Log likelihood = -796.53812  
Iteration 54: Log likelihood = -796.53433  
Iteration 55: Log likelihood = -796.53115  
Iteration 56: Log likelihood =  -796.4982  
Iteration 57: Log likelihood = -795.95469  
Iteration 58: Log likelihood = -794.72453  
Iteration 59: Log likelihood = -794.65087  
Iteration 60: Log likelihood = -794.64679  
Iteration 61: Log likelihood = -794.58623  
Iteration 62: Log likelihood = -793.92147  
Iteration 63: Log likelihood = -792.97471  
Iteration 64: Log likelihood = -792.84117  
Iteration 65: Log likelihood = -792.83889  
Iteration 66: Log likelihood =  -792.8139  
Iteration 67: Log likelihood = -792.33746  
Iteration 68: Log likelihood =  -791.5832  
Iteration 69: Log likelihood = -791.51038  
Iteration 70: Log likelihood = -791.50951  
Iteration 71: Log likelihood = -791.50934  
Iteration 72: Log likelihood = -791.48563  
Iteration 73: Log likelihood = -791.43462  
Iteration 74: Log likelihood = -791.42497  
Iteration 75: Log likelihood = -791.42474  
Iteration 76: Log likelihood = -791.42332  
Iteration 77: Log likelihood = -791.19427  
Iteration 78: Log likelihood =  -791.0113  
Iteration 79: Log likelihood = -791.00158  
Iteration 80: Log likelihood = -791.00145  
Iteration 81: Log likelihood = -791.00144  
Iteration 82: Log likelihood =  -791.0013  
Iteration 83: Log likelihood =  -790.9578  
Iteration 84: Log likelihood = -790.94457  
Iteration 85: Log likelihood = -790.94445  
Iteration 86: Log likelihood = -790.94445  
Iteration 87: Log likelihood = -790.94443  
Iteration 88: Log likelihood = -790.94274  
Iteration 89: Log likelihood = -790.93471  
Iteration 90: Log likelihood = -790.93022  
Iteration 91: Log likelihood = -790.93012  
Iteration 92: Log likelihood = -790.93012  
Iteration 93: Log likelihood = -790.93011  
Iteration 94: Log likelihood = -790.92994  
Iteration 95: Log likelihood = -790.92778  
Iteration 96: Log likelihood = -790.91987  
Iteration 97: Log likelihood = -790.91591  
Iteration 98: Log likelihood = -790.91579  
Iteration 99: Log likelihood = -790.91578  
Iteration 100: Log likelihood = -790.91574  
Iteration 101: Log likelihood = -790.91402  
Iteration 102: Log likelihood =  -790.8991  
Iteration 103: Log likelihood =  -790.8925  
Iteration 104: Log likelihood = -790.89245  
Iteration 105: Log likelihood = -790.89245  
Iteration 106: Log likelihood = -790.89243  
Iteration 107: Log likelihood = -790.88982  
Iteration 108: Log likelihood = -790.88972  
Iteration 109: Log likelihood = -790.88972  
Iteration 110: Log likelihood = -790.88972  
Iteration 111: Log likelihood = -790.88972  
Iteration 112: Log likelihood = -790.88971  
Iteration 113: Log likelihood = -790.88962  
Iteration 114: Log likelihood = -790.88953  

                                                           Number of obs = 673
                                                           Wald chi2(0)  =   .
Log likelihood = -790.88953                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
--------------------------------------------------------------------------------
               | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
hosp_s         |
       tot_pop |          1  (constrained)
---------------+----------------------------------------------------------------
hosp_v         |
         rural |   117.4956   45.36156     2.59   0.010     28.58858    206.4026
     income_pc |   .6054059   8.899409     0.07   0.946    -16.83712    18.04793
      hispanic |  -102.8995   32.60307    -3.16   0.002    -166.8003   -38.99864
 nonhisp_black |   422.3076   242.0265     1.74   0.081    -52.05556    896.6708
gte_highschool |  -206.2814   156.3499    -1.32   0.187    -512.7215    100.1588
        age_65 |   356.1844   154.0067     2.31   0.021     54.33685    658.0319
     uninsured |   101.6091   151.5878     0.67   0.503    -195.4975    398.7157
---------------+----------------------------------------------------------------
hosp_f         |
cms_wage_index |   .9239555   .5412545     1.71   0.088    -.1368839    1.984795
 con_intensity |    .723939   .2469378     2.93   0.003     .2399498    1.207928
---------------+----------------------------------------------------------------
      /hosp_a1 |   143.3809    66.4609     2.16   0.031      13.1199    273.6418
      /hosp_g1 |   .4425351   .5283566     0.84   0.402    -.5930249    1.478095
---------------+----------------------------------------------------------------
ucc_s          |
      tot_pop2 |          1  (constrained)
---------------+----------------------------------------------------------------
ucc_v          |
   n_hospitals |   -66.9618   19.17169    -3.49   0.000    -104.5376   -29.38598
         rural |   59.49997   42.81289     1.39   0.165    -24.41175    143.4117
     income_pc |  -33.16756   11.14092    -2.98   0.003    -55.00337   -11.33176
      hispanic |  -77.70067   48.63423    -1.60   0.110     -173.022    17.62066
 nonhisp_black |  -238.8794   240.7091    -0.99   0.321    -710.6606    232.9018
gte_highschool |   315.0203   225.8161     1.40   0.163    -127.5712    757.6117
        age_65 |   294.2851   184.2684     1.60   0.110    -66.87425    655.4445
     uninsured |   110.9519   185.4792     0.60   0.550    -252.5806    474.4844
---------------+----------------------------------------------------------------
ucc_f          |
cms_wage_index |   .2780596   .4521958     0.61   0.539    -.6082279    1.164347
---------------+----------------------------------------------------------------
       /ucc_a1 |   390.0948    102.006     3.82   0.000     190.1668    590.0228
       /ucc_a2 |   254.7905   39.05001     6.52   0.000     178.2539    331.3271
       /ucc_a3 |   3.362319   10.00377     0.34   0.737    -16.24471    22.96934
       /ucc_g1 |   1.280193    .450294     2.84   0.004     .3976328    2.162753
       /ucc_g2 |    .052813   .1075274     0.49   0.623    -.1579368    .2635629
       /ucc_g3 |   .5036007   .0966514     5.21   0.000     .3141674    .6930341
            /r |    .345186   .1123441     3.07   0.002     .1249957    .5653763
--------------------------------------------------------------------------------
(est2 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. do "BR_EntryThreshold_Bi.do" 10000 10000 "A"

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{    

. 
end of do-file

. do "BR_EntryThreshold_Bi_Conditional.do" 10000 10000 0

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{    

. 
. disp "UCC"
UCC

. matrix list t_monopoly

symmetric t_monopoly[1,1]
        _cons
r1  30.313972

. matrix list se_monopoly

symmetric se_monopoly[1,1]
           c1
r1  1.4440626

. matrix list t_duopoly

symmetric t_duopoly[1,1]
        _cons
r1  32.177006

. matrix list se_duopoly

symmetric se_duopoly[1,1]
          c1
r1  1.865971

. matrix list t_nfirms3

symmetric t_nfirms3[1,1]
        _cons
r1  30.974848

. matrix list se_f3

symmetric se_f3[1,1]
           c1
r1  1.9266549

. matrix list t_2f_1f

symmetric t_2f_1f[1,1]
           c1
r1  1.0614579

. matrix list se_t21

symmetric se_t21[1,1]
           c1
r1  .07229276

. matrix list t_3f_2f

symmetric t_3f_2f[1,1]
           c1
r1  .96263921

. matrix list se_t32

symmetric se_t32[1,1]
          c1
r1  .0311452

. 
end of do-file

. do "BR_EntryThreshold_Bi_Conditional.do" 10000 10000 1

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{    

. 
. disp "UCC"
UCC

. matrix list t_monopoly

symmetric t_monopoly[1,1]
       _cons
r1  34.90857

. matrix list se_monopoly

symmetric se_monopoly[1,1]
           c1
r1  2.2809051

. matrix list t_duopoly

symmetric t_duopoly[1,1]
        _cons
r1  44.086798

. matrix list se_duopoly

symmetric se_duopoly[1,1]
           c1
r1  3.1165225

. matrix list t_nfirms3

symmetric t_nfirms3[1,1]
        _cons
r1  44.012629

. matrix list se_f3

symmetric se_f3[1,1]
           c1
r1  2.5527393

. matrix list t_2f_1f

symmetric t_2f_1f[1,1]
           c1
r1  1.2629219

. matrix list se_t21

symmetric se_t21[1,1]
         c1
r1  .098734

. matrix list t_3f_2f

symmetric t_3f_2f[1,1]
           c1
r1  .99831764

. matrix list se_t32

symmetric se_t32[1,1]
           c1
r1  .03522873

. 
end of do-file

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.333006   .4460353     2.99   0.003     .4587928    2.207219
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.836607   .4516602     4.07   0.000     .9513689    2.721844
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   135.3043   96.83861     1.40   0.162     -54.4959    325.1045
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    131.942   97.67996     1.35   0.177    -59.50725    323.3912
------------------------------------------------------------------------------

. nlcom atanh(/r)

       _nl_1: atanh(/r)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .3599681    .127541     2.82   0.005     .1099924    .6099439
------------------------------------------------------------------------------

. 
. estout *, drop(tot_pop*) cells("b(fmt(1)) se(fmt(1))") label mlabels("Univariate" "Bivariate")

------------------------------------------------------------------------
                       Univariate                 Bivariate             
                                b           se            b           se
------------------------------------------------------------------------
ucc_v                                                                   
Additional hospita~e        -23.6         14.4        -67.0         19.2
Rural                        52.2         43.5         59.5         42.8
Income per capita           -39.9         11.3        -33.2         11.1
Hispanic                    -69.3         51.0        -77.7         48.6
Black                      -300.9        242.3       -238.9        240.7
High school or more         382.8        232.6        315.0        225.8
Age 65 or more              308.0        189.5        294.3        184.3
Uninsured                    93.7        192.6        111.0        185.5
------------------------------------------------------------------------
ucc_f                                                                   
CMS wage index                0.2          0.5          0.3          0.5
------------------------------------------------------------------------
/                                                                       
ucc_a1                      373.5        104.6                          
ucc_a2                      279.5         37.5                          
ucc_a3                        5.4         10.8                          
ucc_g1                        1.3          0.5                          
ucc_g2                        0.0          0.1                          
ucc_g3                        0.5          0.1                          
hosp_a1                                               143.4         66.5
hosp_g1                                                 0.4          0.5
------------------------------------------------------------------------
hosp_v                                                                  
Rural                                                 117.5         45.4
Income per capita                                       0.6          8.9
Hispanic                                             -102.9         32.6
Black                                                 422.3        242.0
High school or more                                  -206.3        156.3
Age 65 or more                                        356.2        154.0
Uninsured                                             101.6        151.6
------------------------------------------------------------------------
hosp_f                                                                  
CMS wage index                                          0.9          0.5
CON laws                                                0.7          0.2
------------------------------------------------------------------------
/                                                                       
hosp_a1                                               143.4         66.5
hosp_g1                                                 0.4          0.5
------------------------------------------------------------------------

. 
. ********************************************************************************
. // Subsample analysis
. ********************************************************************************
. 
. // Income
. 
. ml clear

. ml model lf brentry_bioprobit_neg (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = rural
>  hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (hosp_f:cat_hosp2 = cms_wage_in
> dex con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (ucc_v:cat_ucc =
>  n_hospitals rural hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (ucc_f:cat_uc
> c = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3 /r if high_income==0
> , constraints(1 2)

. eststo clear

. eststo: ml max, difficult iterate(100) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -706.61809
Rescale:      Log likelihood = -619.08041
Rescale eq:   Log likelihood = -434.31589
Iteration 0:  Log likelihood = -716.36325  (not concave)
Iteration 1:  Log likelihood =  -462.2571  (not concave)
Iteration 2:  Log likelihood = -402.35643  
Iteration 3:  Log likelihood = -384.20039  
Iteration 4:  Log likelihood = -382.41389  
Iteration 5:  Log likelihood = -382.27361  
Iteration 6:  Log likelihood = -382.27349  

                                                           Number of obs = 337
                                                           Wald chi2(0)  =   .
Log likelihood = -382.27349                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
--------------------------------------------------------------------------------
               | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
hosp_s         |
       tot_pop |          1  (constrained)
---------------+----------------------------------------------------------------
hosp_v         |
         rural |   79.14962   69.98194     1.13   0.258    -58.01248    216.3117
      hispanic |  -86.47921   65.35839    -1.32   0.186    -214.5793    41.62089
 nonhisp_black |   513.8073   271.5417     1.89   0.058    -18.40472    1046.019
gte_highschool |   1.549399   313.9576     0.00   0.996    -613.7962     616.895
        age_65 |    375.384   277.7637     1.35   0.177    -169.0228    919.7909
     uninsured |   50.93494   255.2889     0.20   0.842     -449.422    551.2919
---------------+----------------------------------------------------------------
hosp_f         |
cms_wage_index |   .7664116   .9257373     0.83   0.408       -1.048    2.580823
 con_intensity |   .5562496   .3746606     1.48   0.138    -.1780717    1.290571
---------------+----------------------------------------------------------------
      /hosp_a1 |   88.94054   162.1748     0.55   0.583    -228.9162    406.7973
      /hosp_g1 |   .8966857   .8926017     1.00   0.315    -.8527814    2.646153
---------------+----------------------------------------------------------------
ucc_s          |
      tot_pop2 |          1  (constrained)
---------------+----------------------------------------------------------------
ucc_v          |
   n_hospitals |   -71.8821    34.5821    -2.08   0.038    -139.6618   -4.102428
         rural |   175.8748   64.95985     2.71   0.007     48.55582    303.1938
      hispanic |   110.3441   86.05389     1.28   0.200    -58.31845    279.0066
 nonhisp_black |  -147.1823   265.6728    -0.55   0.580    -667.8913    373.5268
gte_highschool |   1377.142   492.5491     2.80   0.005     411.7632     2342.52
        age_65 |   670.4996     312.59     2.14   0.032     57.83447    1283.165
     uninsured |   164.0537   271.1573     0.61   0.545    -367.4049    695.5123
---------------+----------------------------------------------------------------
ucc_f          |
cms_wage_index |    .059798   .6950877     0.09   0.931    -1.302549    1.422145
---------------+----------------------------------------------------------------
       /ucc_a1 |  -168.7911   232.5337    -0.73   0.468    -624.5488    286.9666
       /ucc_a2 |   315.6029   58.67851     5.38   0.000     200.5952    430.6107
       /ucc_a3 |  -24.16618    19.8764    -1.22   0.224    -63.12321    14.79086
       /ucc_g1 |   1.793691   .6881022     2.61   0.009     .4450357    3.142347
       /ucc_g2 |   .0359416   .1652687     0.22   0.828     -.287979    .3598622
       /ucc_g3 |   .6572361   .1770725     3.71   0.000     .3101803    1.004292
            /r |   .1956456   .1691974     1.16   0.248    -.1359752    .5272665
--------------------------------------------------------------------------------
(est1 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. do "BR_EntryThreshold_Bi_Income.do" 10000 10000 "B" 0

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{    

. 
end of do-file

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.829633   .6739122     2.71   0.007     .5087893    3.150477
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   2.486869   .6886571     3.61   0.000     1.137126    3.836612
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   -484.394    230.057    -2.11   0.035    -935.2974   -33.49067
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -460.2279   231.9062    -1.98   0.047    -914.7557   -5.700001
------------------------------------------------------------------------------

. nlcom atanh(/r)

       _nl_1: atanh(/r)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .1982008   .1759316     1.13   0.260    -.1466187    .5430204
------------------------------------------------------------------------------

. 
. ml clear

. ml model lf brentry_bioprobit_neg (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = rural
>  hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (hosp_f:cat_hosp2 = cms_wage_in
> dex con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (ucc_v:cat_ucc =
>  n_hospitals rural hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (ucc_f:cat_uc
> c = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3 /r if high_income==1
> , constraints(1 2) technique(bfgs)

. eststo: ml max, difficult iterate(300) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -944.55618
Rescale:      Log likelihood =  -684.4657
Rescale eq:   Log likelihood = -428.04629
Iteration 0:  Log likelihood = -986.17471  
Iteration 1:  Log likelihood = -830.53319  (backed up)
Iteration 2:  Log likelihood =  -658.4793  (backed up)
Iteration 3:  Log likelihood = -648.97202  (backed up)
Iteration 4:  Log likelihood = -646.70283  (backed up)
Iteration 5:  Log likelihood = -645.09746  (backed up)
Iteration 6:  Log likelihood =  -637.2277  
Iteration 7:  Log likelihood =   -606.687  
Iteration 8:  Log likelihood = -571.31423  
Iteration 9:  Log likelihood = -552.05889  
Iteration 10: Log likelihood = -528.38442  
Iteration 11: Log likelihood = -522.75438  
Iteration 12: Log likelihood = -491.16538  
Iteration 13: Log likelihood = -471.21904  
Iteration 14: Log likelihood = -460.32135  
Iteration 15: Log likelihood = -455.89996  
Iteration 16: Log likelihood = -451.91647  
Iteration 17: Log likelihood = -450.01291  
Iteration 18: Log likelihood = -448.66947  
Iteration 19: Log likelihood = -447.61955  
Iteration 20: Log likelihood = -446.53272  
Iteration 21: Log likelihood = -443.73948  
Iteration 22: Log likelihood = -435.59349  
Iteration 23: Log likelihood = -431.71158  
Iteration 24: Log likelihood = -430.19185  
Iteration 25: Log likelihood = -429.15514  
Iteration 26: Log likelihood =  -425.2923  
Iteration 27: Log likelihood =   -412.795  
Iteration 28: Log likelihood =   -407.962  
Iteration 29: Log likelihood = -406.80138  
Iteration 30: Log likelihood = -406.63127  
Iteration 31: Log likelihood =  -406.5956  
Iteration 32: Log likelihood = -406.28151  
Iteration 33: Log likelihood = -402.47528  
Iteration 34: Log likelihood = -397.88699  
Iteration 35: Log likelihood =  -397.0545  
Iteration 36: Log likelihood = -396.99932  
Iteration 37: Log likelihood =  -396.9941  
Iteration 38: Log likelihood = -396.98592  
Iteration 39: Log likelihood = -396.81311  
Iteration 40: Log likelihood = -395.87429  
Iteration 41: Log likelihood = -394.92971  
Iteration 42: Log likelihood =  -394.8351  
Iteration 43: Log likelihood = -394.82632  
Iteration 44: Log likelihood = -394.82427  
Iteration 45: Log likelihood = -394.80215  
Iteration 46: Log likelihood = -393.98284  
Iteration 47: Log likelihood = -392.78846  
Iteration 48: Log likelihood =  -392.5201  
Iteration 49: Log likelihood =  -392.5087  
Iteration 50: Log likelihood = -392.50489  
Iteration 51: Log likelihood = -392.45819  
Iteration 52: Log likelihood = -391.47418  
Iteration 53: Log likelihood =  -390.5359  
Iteration 54: Log likelihood = -390.38917  
Iteration 55: Log likelihood = -390.37893  
Iteration 56: Log likelihood = -390.37093  
Iteration 57: Log likelihood = -390.28643  
Iteration 58: Log likelihood = -388.67754  
Iteration 59: Log likelihood = -386.37556  
Iteration 60: Log likelihood = -386.05187  
Iteration 61: Log likelihood = -386.03169  
Iteration 62: Log likelihood = -386.02559  
Iteration 63: Log likelihood = -386.02337  
Iteration 64: Log likelihood = -386.01979  
Iteration 65: Log likelihood = -385.99918  
Iteration 66: Log likelihood = -385.65103  
Iteration 67: Log likelihood = -385.27226  
Iteration 68: Log likelihood = -385.22651  
Iteration 69: Log likelihood = -385.22319  
Iteration 70: Log likelihood =  -385.2229  
Iteration 71: Log likelihood = -385.21969  
Iteration 72: Log likelihood = -384.98062  
Iteration 73: Log likelihood = -384.91313  
Iteration 74: Log likelihood =  -384.9106  
Iteration 75: Log likelihood = -384.91037  
Iteration 76: Log likelihood = -384.91031  
Iteration 77: Log likelihood = -384.90993  
Iteration 78: Log likelihood = -384.88424  
Iteration 79: Log likelihood = -384.68887  
Iteration 80: Log likelihood =   -384.565  
Iteration 81: Log likelihood = -384.56266  
Iteration 82: Log likelihood = -384.56253  
Iteration 83: Log likelihood = -384.56252  
Iteration 84: Log likelihood = -384.56234  
Iteration 85: Log likelihood = -384.55605  
Iteration 86: Log likelihood = -384.42421  
Iteration 87: Log likelihood = -384.24734  
Iteration 88: Log likelihood = -384.23095  
Iteration 89: Log likelihood = -384.23084  
Iteration 90: Log likelihood = -384.23083  
Iteration 91: Log likelihood = -384.23065  
Iteration 92: Log likelihood = -384.22845  
Iteration 93: Log likelihood = -384.20489  
Iteration 94: Log likelihood = -384.02223  
Iteration 95: Log likelihood = -384.01062  
Iteration 96: Log likelihood = -384.01054  
Iteration 97: Log likelihood = -384.01051  
Iteration 98: Log likelihood = -384.01004  
Iteration 99: Log likelihood = -384.00279  
Iteration 100: Log likelihood = -383.91234  
Iteration 101: Log likelihood = -383.87876  
Iteration 102: Log likelihood = -383.87828  
Iteration 103: Log likelihood = -383.87828  
Iteration 104: Log likelihood = -383.87828  
Iteration 105: Log likelihood = -383.87825  
Iteration 106: Log likelihood = -383.87622  
Iteration 107: Log likelihood = -383.87005  
Iteration 108: Log likelihood = -383.86988  
Iteration 109: Log likelihood = -383.86988  
Iteration 110: Log likelihood = -383.86987  
Iteration 111: Log likelihood = -383.86973  
Iteration 112: Log likelihood = -383.86844  
Iteration 113: Log likelihood = -383.86839  
Iteration 114: Log likelihood = -383.86839  
Iteration 115: Log likelihood = -383.86839  
Iteration 116: Log likelihood = -383.86838  
Iteration 117: Log likelihood =  -383.8683  
BFGS stepping has contracted, resetting BFGS Hessian
Iteration 118: Log likelihood = -383.86675  
Iteration 119: Log likelihood = -383.86675  (backed up)
Iteration 120: Log likelihood = -383.86675  (backed up)
Iteration 121: Log likelihood = -383.86675  
Iteration 122: Log likelihood = -383.86675  
Iteration 123: Log likelihood = -383.86675  
Iteration 124: Log likelihood = -383.86675  
Iteration 125: Log likelihood = -383.86675  
Iteration 126: Log likelihood = -383.86675  
Iteration 127: Log likelihood = -383.86675  
Iteration 128: Log likelihood = -383.86675  
Iteration 129: Log likelihood = -383.86675  
Iteration 130: Log likelihood = -383.86675  
Iteration 131: Log likelihood = -383.86675  
Iteration 132: Log likelihood = -383.86675  
Iteration 133: Log likelihood = -383.86675  
Iteration 134: Log likelihood = -383.86674  
Iteration 135: Log likelihood = -383.86674  
Iteration 136: Log likelihood = -383.86674  
Iteration 137: Log likelihood = -383.86674  
Iteration 138: Log likelihood = -383.86674  
Iteration 139: Log likelihood = -383.86674  
Iteration 140: Log likelihood = -383.86674  
Iteration 141: Log likelihood = -383.86674  
Iteration 142: Log likelihood = -383.86673  
Iteration 143: Log likelihood = -383.86673  
Iteration 144: Log likelihood = -383.86673  
Iteration 145: Log likelihood = -383.86673  
Iteration 146: Log likelihood = -383.86672  
Iteration 147: Log likelihood = -383.86671  
Iteration 148: Log likelihood = -383.86671  
Iteration 149: Log likelihood = -383.86671  
Iteration 150: Log likelihood = -383.86671  
Iteration 151: Log likelihood =  -383.8667  
Iteration 152: Log likelihood =  -383.8667  
Iteration 153: Log likelihood =  -383.8667  
Iteration 154: Log likelihood =  -383.8667  
Iteration 155: Log likelihood =  -383.8667  
Iteration 156: Log likelihood =  -383.8667  
Iteration 157: Log likelihood =  -383.8667  
Iteration 158: Log likelihood =  -383.8667  
Iteration 159: Log likelihood = -383.86669  
Iteration 160: Log likelihood = -383.86669  
Iteration 161: Log likelihood = -383.86669  
Iteration 162: Log likelihood = -383.86669  
Iteration 163: Log likelihood = -383.86669  
Iteration 164: Log likelihood = -383.86667  
Iteration 165: Log likelihood = -383.86667  
Iteration 166: Log likelihood = -383.86667  
Iteration 167: Log likelihood = -383.86667  
Iteration 168: Log likelihood = -383.86667  
Iteration 169: Log likelihood = -383.86667  
Iteration 170: Log likelihood = -383.86667  
Iteration 171: Log likelihood = -383.86667  
Iteration 172: Log likelihood = -383.86665  
Iteration 173: Log likelihood = -383.86664  
Iteration 174: Log likelihood = -383.86664  
Iteration 175: Log likelihood = -383.86664  
Iteration 176: Log likelihood = -383.86664  
Iteration 177: Log likelihood = -383.86662  
Iteration 178: Log likelihood = -383.86661  

                                                           Number of obs = 336
                                                           Wald chi2(0)  =   .
Log likelihood = -383.86661                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
--------------------------------------------------------------------------------
               | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
hosp_s         |
       tot_pop |          1  (constrained)
---------------+----------------------------------------------------------------
hosp_v         |
         rural |   147.8461    63.6925     2.32   0.020     23.01114    272.6812
      hispanic |  -94.25257   34.74579    -2.71   0.007    -162.3531   -26.15207
 nonhisp_black |   704.7297   799.5554     0.88   0.378      -862.37    2271.829
gte_highschool |   -72.8617   145.8211    -0.50   0.617    -358.6658    212.9424
        age_65 |   344.3367   189.4734     1.82   0.069    -27.02437    715.6979
     uninsured |   72.51603   178.9664     0.41   0.685    -278.2517    423.2838
---------------+----------------------------------------------------------------
hosp_f         |
cms_wage_index |   1.274361    .696788     1.83   0.067    -.0913183     2.64004
 con_intensity |   .8259443    .350623     2.36   0.018     .1387358    1.513153
---------------+----------------------------------------------------------------
      /hosp_a1 |   69.87671   81.18621     0.86   0.389    -89.24534    228.9988
      /hosp_g1 |  -.1320435   .6884483    -0.19   0.848    -1.481377     1.21729
---------------+----------------------------------------------------------------
ucc_s          |
      tot_pop2 |          1  (constrained)
---------------+----------------------------------------------------------------
ucc_v          |
   n_hospitals |  -57.39942   25.82141    -2.22   0.026    -108.0085   -6.790383
         rural |  -4.277437   61.76637    -0.07   0.945    -125.3373    116.7824
      hispanic |  -212.5634   53.19149    -4.00   0.000    -316.8168     -108.31
 nonhisp_black |  -606.5963   707.1283    -0.86   0.391    -1992.542    779.3497
gte_highschool |  -596.5024   182.0189    -3.28   0.001    -953.2529   -239.7519
        age_65 |  -110.1414   257.6509    -0.43   0.669    -615.1278    394.8451
     uninsured |   608.9138   307.0708     1.98   0.047     7.066125    1210.761
---------------+----------------------------------------------------------------
ucc_f          |
cms_wage_index |   .4616666   .6406733     0.72   0.471      -.79403    1.717363
---------------+----------------------------------------------------------------
       /ucc_a1 |   703.1012   127.0165     5.54   0.000     454.1534     952.049
       /ucc_a2 |   194.9036   52.82127     3.69   0.000     91.37581    298.4314
       /ucc_a3 |   23.48825   19.66905     1.19   0.232    -15.06238    62.03889
       /ucc_g1 |    .894425   .6459254     1.38   0.166    -.3715656    2.160416
       /ucc_g2 |   .0979443   .1469929     0.67   0.505    -.1901565    .3860451
       /ucc_g3 |   .4546388   .1544619     2.94   0.003     .1518991    .7573785
            /r |   .4321947   .1747168     2.47   0.013      .089756    .7746333
--------------------------------------------------------------------------------
(est2 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. do "BR_EntryThreshold_Bi_Income.do" 10000 10000 "C" 1

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{    

. 
end of do-file

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9923694   .6491495     1.53   0.126    -.2799402    2.264679
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.447008   .6533562     2.21   0.027     .1664535    2.727563
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   508.1976   113.6988     4.47   0.000     285.3521    731.0432
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   484.7093   113.3387     4.28   0.000     262.5696    706.8491
------------------------------------------------------------------------------

. nlcom atanh(/r)

       _nl_1: atanh(/r)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .4625923   .2148489     2.15   0.031     .0414962    .8836884
------------------------------------------------------------------------------

. 
. // SVI categories
. 
. ml clear

. ml model lf brentry_bioprobit_neg (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = rural
>  income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (hosp_f:cat_hosp2 = c
> ms_wage_index con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (ucc_v
> :cat_ucc = n_hospitals rural income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, n
> ocons) (ucc_f:cat_ucc = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3 
> /r if high_svi==1, constraints(1 2)

. eststo: ml max, difficult iterate(300) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -876.89537
Rescale:      Log likelihood = -688.50587
Rescale eq:   Log likelihood = -417.17743
Iteration 0:  Log likelihood = -996.03924  (not concave)
Iteration 1:  Log likelihood = -598.96161  (not concave)
Iteration 2:  Log likelihood = -558.15479  (not concave)
Iteration 3:  Log likelihood = -536.15826  (not concave)
Iteration 4:  Log likelihood = -515.78991  (not concave)
Iteration 5:  Log likelihood = -510.18282  (not concave)
Iteration 6:  Log likelihood = -504.90632  (not concave)
Iteration 7:  Log likelihood = -500.40209  (not concave)
Iteration 8:  Log likelihood = -497.24753  (not concave)
Iteration 9:  Log likelihood = -497.19402  (not concave)
Iteration 10: Log likelihood = -496.89196  (not concave)
Iteration 11: Log likelihood = -482.62591  (not concave)
Iteration 12: Log likelihood = -481.03617  (not concave)
Iteration 13: Log likelihood = -480.28724  (not concave)
Iteration 14: Log likelihood = -479.76634  (not concave)
Iteration 15: Log likelihood = -479.56654  (not concave)
Iteration 16: Log likelihood = -479.32539  (not concave)
Iteration 17: Log likelihood = -472.83788  (not concave)
Iteration 18: Log likelihood = -471.87937  (not concave)
Iteration 19: Log likelihood = -471.19505  (not concave)
Iteration 20: Log likelihood = -471.03385  (not concave)
Iteration 21: Log likelihood = -470.80354  (not concave)
Iteration 22: Log likelihood = -470.67437  (not concave)
Iteration 23: Log likelihood = -470.49663  (not concave)
Iteration 24: Log likelihood = -470.26702  (not concave)
Iteration 25: Log likelihood = -470.18415  (not concave)
Iteration 26: Log likelihood = -470.10052  (not concave)
Iteration 27: Log likelihood = -470.03772  (not concave)
Iteration 28: Log likelihood =  -469.9125  (not concave)
Iteration 29: Log likelihood = -468.17601  (not concave)
Iteration 30: Log likelihood = -467.70569  (not concave)
Iteration 31: Log likelihood = -467.60236  (not concave)
Iteration 32: Log likelihood = -467.55521  (not concave)
Iteration 33: Log likelihood =  -467.4084  (not concave)
Iteration 34: Log likelihood = -467.28177  (not concave)
Iteration 35: Log likelihood = -467.06151  (not concave)
Iteration 36: Log likelihood = -466.95492  (not concave)
Iteration 37: Log likelihood = -466.83261  (not concave)
Iteration 38: Log likelihood = -466.77773  (not concave)
Iteration 39: Log likelihood =  -466.3035  (not concave)
Iteration 40: Log likelihood = -466.10947  (not concave)
Iteration 41: Log likelihood = -466.06803  (not concave)
Iteration 42: Log likelihood = -464.12729  (not concave)
Iteration 43: Log likelihood = -463.46018  (not concave)
Iteration 44: Log likelihood = -463.10422  (not concave)
Iteration 45: Log likelihood = -463.00682  (not concave)
Iteration 46: Log likelihood = -462.92206  (not concave)
Iteration 47: Log likelihood = -462.88012  (not concave)
Iteration 48: Log likelihood = -462.70088  (not concave)
Iteration 49: Log likelihood = -462.63295  (not concave)
Iteration 50: Log likelihood = -462.50017  (not concave)
Iteration 51: Log likelihood = -462.46664  (not concave)
Iteration 52: Log likelihood = -462.43669  (not concave)
Iteration 53: Log likelihood = -462.41688  (not concave)
Iteration 54: Log likelihood = -462.15936  (not concave)
Iteration 55: Log likelihood =  -462.0664  (not concave)
Iteration 56: Log likelihood = -461.85468  (not concave)
Iteration 57: Log likelihood = -461.84233  (not concave)
Iteration 58: Log likelihood = -461.71529  (not concave)
Iteration 59: Log likelihood = -461.67801  (not concave)
Iteration 60: Log likelihood = -461.62801  (not concave)
Iteration 61: Log likelihood = -461.57933  (not concave)
Iteration 62: Log likelihood = -461.43871  (not concave)
Iteration 63: Log likelihood = -461.37126  (not concave)
Iteration 64: Log likelihood = -461.29723  (not concave)
Iteration 65: Log likelihood = -461.28112  (not concave)
Iteration 66: Log likelihood = -461.21741  (not concave)
Iteration 67: Log likelihood = -461.18873  (not concave)
Iteration 68: Log likelihood = -461.14706  (not concave)
Iteration 69: Log likelihood = -461.06248  (not concave)
Iteration 70: Log likelihood = -461.02913  (not concave)
Iteration 71: Log likelihood = -460.99089  (not concave)
Iteration 72: Log likelihood = -460.96593  (not concave)
Iteration 73: Log likelihood = -460.94385  (not concave)
Iteration 74: Log likelihood = -460.62633  (not concave)
Iteration 75: Log likelihood =  -460.5265  (not concave)
Iteration 76: Log likelihood = -460.50286  (not concave)
Iteration 77: Log likelihood = -460.44592  (not concave)
Iteration 78: Log likelihood = -460.14206  (not concave)
Iteration 79: Log likelihood = -460.06992  (not concave)
Iteration 80: Log likelihood = -460.02114  (not concave)
Iteration 81: Log likelihood = -459.89908  (not concave)
Iteration 82: Log likelihood = -459.88206  (not concave)
Iteration 83: Log likelihood =  -459.8802  (not concave)
Iteration 84: Log likelihood = -459.80223  (not concave)
Iteration 85: Log likelihood = -459.73037  (not concave)
Iteration 86: Log likelihood = -459.72391  (not concave)
Iteration 87: Log likelihood =  -459.7064  (not concave)
Iteration 88: Log likelihood = -459.63076  (not concave)
Iteration 89: Log likelihood = -459.27398  (not concave)
Iteration 90: Log likelihood = -459.10823  (not concave)
Iteration 91: Log likelihood = -459.09392  (not concave)
Iteration 92: Log likelihood = -458.97425  (not concave)
Iteration 93: Log likelihood = -458.85208  (not concave)
Iteration 94: Log likelihood = -458.11942  (not concave)
Iteration 95: Log likelihood = -457.84998  (not concave)
Iteration 96: Log likelihood = -457.79233  (not concave)
Iteration 97: Log likelihood = -457.68095  (not concave)
Iteration 98: Log likelihood = -457.51426  (not concave)
Iteration 99: Log likelihood = -457.46057  (not concave)
Iteration 100: Log likelihood = -457.14684  (not concave)
Iteration 101: Log likelihood = -456.99312  (not concave)
Iteration 102: Log likelihood = -456.87554  (not concave)
Iteration 103: Log likelihood = -450.87189  (not concave)
Iteration 104: Log likelihood = -450.07877  (not concave)
Iteration 105: Log likelihood = -450.04939  (not concave)
Iteration 106: Log likelihood = -449.68243  (not concave)
Iteration 107: Log likelihood = -449.42106  (not concave)
Iteration 108: Log likelihood = -449.17345  (not concave)
Iteration 109: Log likelihood = -449.00153  (not concave)
Iteration 110: Log likelihood = -448.80803  (not concave)
Iteration 111: Log likelihood = -448.70769  (not concave)
Iteration 112: Log likelihood = -448.52222  (not concave)
Iteration 113: Log likelihood = -446.96473  (not concave)
Iteration 114: Log likelihood = -446.31385  (not concave)
Iteration 115: Log likelihood = -446.13553  (not concave)
Iteration 116: Log likelihood = -445.89833  (not concave)
Iteration 117: Log likelihood = -445.75722  (not concave)
Iteration 118: Log likelihood = -445.72733  (not concave)
Iteration 119: Log likelihood = -444.17364  (not concave)
Iteration 120: Log likelihood = -443.84632  (not concave)
Iteration 121: Log likelihood = -443.80636  (not concave)
Iteration 122: Log likelihood = -443.65978  (not concave)
Iteration 123: Log likelihood = -443.24646  (not concave)
Iteration 124: Log likelihood = -442.95859  (not concave)
Iteration 125: Log likelihood = -442.80022  (not concave)
Iteration 126: Log likelihood = -442.73245  (not concave)
Iteration 127: Log likelihood = -442.67657  (not concave)
Iteration 128: Log likelihood =  -442.4444  (not concave)
Iteration 129: Log likelihood = -441.58574  (not concave)
Iteration 130: Log likelihood = -441.41301  (not concave)
Iteration 131: Log likelihood = -441.32228  (not concave)
Iteration 132: Log likelihood = -441.13525  (not concave)
Iteration 133: Log likelihood = -441.07073  (not concave)
Iteration 134: Log likelihood = -441.02619  (not concave)
Iteration 135: Log likelihood = -440.85802  (not concave)
Iteration 136: Log likelihood = -440.79108  (not concave)
Iteration 137: Log likelihood = -440.75537  (not concave)
Iteration 138: Log likelihood = -440.71731  (not concave)
Iteration 139: Log likelihood = -440.70008  (not concave)
Iteration 140: Log likelihood = -440.59354  (not concave)
Iteration 141: Log likelihood = -440.47935  (not concave)
Iteration 142: Log likelihood = -440.40932  (not concave)
Iteration 143: Log likelihood =   -440.272  (not concave)
Iteration 144: Log likelihood = -440.20188  (not concave)
Iteration 145: Log likelihood = -440.09249  (not concave)
Iteration 146: Log likelihood = -439.69779  (not concave)
Iteration 147: Log likelihood = -439.64132  (not concave)
Iteration 148: Log likelihood = -439.57722  (not concave)
Iteration 149: Log likelihood = -439.55783  (not concave)
Iteration 150: Log likelihood = -439.50191  (not concave)
Iteration 151: Log likelihood = -439.30008  (not concave)
Iteration 152: Log likelihood = -439.21004  (not concave)
Iteration 153: Log likelihood = -439.15611  (not concave)
Iteration 154: Log likelihood = -439.10737  (not concave)
Iteration 155: Log likelihood = -439.07056  (not concave)
Iteration 156: Log likelihood = -438.86642  (not concave)
Iteration 157: Log likelihood = -438.78751  (not concave)
Iteration 158: Log likelihood = -438.38508  (not concave)
Iteration 159: Log likelihood = -438.15875  (not concave)
Iteration 160: Log likelihood = -437.60513  (not concave)
Iteration 161: Log likelihood = -437.41268  (not concave)
Iteration 162: Log likelihood = -437.26986  (not concave)
Iteration 163: Log likelihood = -437.18524  (not concave)
Iteration 164: Log likelihood = -437.10176  (not concave)
Iteration 165: Log likelihood = -437.02549  (not concave)
Iteration 166: Log likelihood = -436.27379  (not concave)
Iteration 167: Log likelihood = -436.06855  (not concave)
Iteration 168: Log likelihood = -436.01201  (not concave)
Iteration 169: Log likelihood = -435.87778  (not concave)
Iteration 170: Log likelihood = -435.80369  (not concave)
Iteration 171: Log likelihood = -435.76504  (not concave)
Iteration 172: Log likelihood = -435.38525  (not concave)
Iteration 173: Log likelihood = -435.30285  (not concave)
Iteration 174: Log likelihood = -435.18177  (not concave)
Iteration 175: Log likelihood = -435.03529  (not concave)
Iteration 176: Log likelihood =  -434.9226  (not concave)
Iteration 177: Log likelihood = -434.87801  (not concave)
Iteration 178: Log likelihood = -434.62697  (not concave)
Iteration 179: Log likelihood = -424.88812  (not concave)
Iteration 180: Log likelihood = -423.97474  (not concave)
Iteration 181: Log likelihood = -423.53139  (not concave)
Iteration 182: Log likelihood = -423.41524  (not concave)
Iteration 183: Log likelihood = -423.21217  (not concave)
Iteration 184: Log likelihood = -423.13321  (not concave)
Iteration 185: Log likelihood = -423.08429  (not concave)
Iteration 186: Log likelihood = -422.97861  (not concave)
Iteration 187: Log likelihood = -422.86731  (not concave)
Iteration 188: Log likelihood = -422.67644  (not concave)
Iteration 189: Log likelihood =  -422.5764  (not concave)
Iteration 190: Log likelihood =  -422.4559  (not concave)
Iteration 191: Log likelihood = -422.29379  (not concave)
Iteration 192: Log likelihood = -421.84838  (not concave)
Iteration 193: Log likelihood = -421.75281  (not concave)
Iteration 194: Log likelihood =  -421.5281  (not concave)
Iteration 195: Log likelihood = -421.47819  (not concave)
Iteration 196: Log likelihood = -421.08905  (not concave)
Iteration 197: Log likelihood = -420.53865  (not concave)
Iteration 198: Log likelihood = -419.99486  (not concave)
Iteration 199: Log likelihood = -419.83529  (not concave)
Iteration 200: Log likelihood =  -419.3765  (not concave)
Iteration 201: Log likelihood = -419.30479  (not concave)
Iteration 202: Log likelihood = -418.88928  (not concave)
Iteration 203: Log likelihood = -418.55231  (not concave)
Iteration 204: Log likelihood = -418.39863  (not concave)
Iteration 205: Log likelihood = -418.23897  (not concave)
Iteration 206: Log likelihood = -418.19892  (not concave)
Iteration 207: Log likelihood =  -417.8667  (not concave)
Iteration 208: Log likelihood = -417.69745  (not concave)
Iteration 209: Log likelihood = -417.29698  (not concave)
Iteration 210: Log likelihood = -417.21012  (not concave)
Iteration 211: Log likelihood = -417.11378  (not concave)
Iteration 212: Log likelihood =  -416.9923  (not concave)
Iteration 213: Log likelihood = -416.92873  (not concave)
Iteration 214: Log likelihood = -416.76272  (not concave)
Iteration 215: Log likelihood = -416.70929  (not concave)
Iteration 216: Log likelihood = -416.65893  (not concave)
Iteration 217: Log likelihood =  -416.6154  (not concave)
Iteration 218: Log likelihood = -416.57051  (not concave)
Iteration 219: Log likelihood = -416.48607  (not concave)
Iteration 220: Log likelihood = -416.29417  (not concave)
Iteration 221: Log likelihood = -416.22933  (not concave)
Iteration 222: Log likelihood = -416.17194  (not concave)
Iteration 223: Log likelihood = -415.65505  (not concave)
Iteration 224: Log likelihood = -415.21956  (not concave)
Iteration 225: Log likelihood = -415.07798  (not concave)
Iteration 226: Log likelihood =  -414.9276  (not concave)
Iteration 227: Log likelihood = -414.69622  (not concave)
Iteration 228: Log likelihood =  -414.4506  (not concave)
Iteration 229: Log likelihood = -413.10424  (not concave)
Iteration 230: Log likelihood = -399.55082  (not concave)
Iteration 231: Log likelihood = -392.19547  (not concave)
Iteration 232: Log likelihood = -380.53166  
Iteration 233: Log likelihood = -370.81689  
Iteration 234: Log likelihood = -370.41998  
Iteration 235: Log likelihood = -370.41324  
Iteration 236: Log likelihood = -370.41322  

                                                           Number of obs = 337
                                                           Wald chi2(0)  =   .
Log likelihood = -370.41322                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
--------------------------------------------------------------------------------
               | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
hosp_s         |
       tot_pop |          1  (constrained)
---------------+----------------------------------------------------------------
hosp_v         |
         rural |   117.0327   60.46641     1.94   0.053    -1.479324    235.5447
     income_pc |   .7028729   13.15894     0.05   0.957    -25.08818    26.49393
      hispanic |  -63.70816    49.9296    -1.28   0.202    -161.5684    34.15205
 nonhisp_black |   421.0848   271.1131     1.55   0.120    -110.2872    952.4568
gte_highschool |   -213.958   225.5651    -0.95   0.343    -656.0574    228.1414
        age_65 |    332.878   208.0406     1.60   0.110    -74.87399      740.63
     uninsured |  -6.486552   201.9789    -0.03   0.974    -402.3579    389.3848
---------------+----------------------------------------------------------------
hosp_f         |
cms_wage_index |   1.160856   .7599041     1.53   0.127    -.3285287     2.65024
 con_intensity |    .605723   .3596385     1.68   0.092    -.0991555    1.310602
---------------+----------------------------------------------------------------
      /hosp_a1 |   157.0528   100.2132     1.57   0.117    -39.36145     353.467
      /hosp_g1 |    .367154   .7438484     0.49   0.622    -1.090762     1.82507
---------------+----------------------------------------------------------------
ucc_s          |
      tot_pop2 |          1  (constrained)
---------------+----------------------------------------------------------------
ucc_v          |
   n_hospitals |  -93.99053   31.30854    -3.00   0.003    -155.3541   -32.62693
         rural |     128.96   61.04237     2.11   0.035     9.319199    248.6009
     income_pc |   34.10616   36.98225     0.92   0.356    -38.37772      106.59
      hispanic |   84.45805   83.26977     1.01   0.310     -78.7477    247.6638
 nonhisp_black |  -159.1664   269.2731    -0.59   0.554    -686.9319    368.5991
gte_highschool |   1223.898   478.9359     2.56   0.011     285.2008    2162.595
        age_65 |   636.4255   281.7559     2.26   0.024     84.19413    1188.657
     uninsured |   256.5017   263.3484     0.97   0.330    -259.6517    772.6551
---------------+----------------------------------------------------------------
ucc_f          |
cms_wage_index |  -.1259498   .5755606    -0.22   0.827    -1.254028    1.002128
---------------+----------------------------------------------------------------
       /ucc_a1 |  -223.7029    225.343    -0.99   0.321     -665.367    217.9613
       /ucc_a2 |   275.7716   56.25619     4.90   0.000     165.5115    386.0318
       /ucc_a3 |  -54.26238   22.45474    -2.42   0.016    -98.27286   -10.25189
       /ucc_g1 |   1.824009   .5963496     3.06   0.002     .6551852    2.992833
       /ucc_g2 |    .025046   .1539734     0.16   0.871    -.2767364    .3268284
       /ucc_g3 |   .9639068   .2166093     4.45   0.000     .5393603    1.388453
            /r |   .3646994    .172592     2.11   0.035     .0264253    .7029736
--------------------------------------------------------------------------------
(est3 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. do "BR_EntryThreshold_Bi_svi.do" 10000 10000 "D" 1

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{    

. 
. 
end of do-file

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.849055   .5870769     3.15   0.002     .6984054    2.999704
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   2.812962   .6169036     4.56   0.000     1.603853     4.02207
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -499.4745   224.6559    -2.22   0.026    -939.7919   -59.15709
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -445.2121   227.2248    -1.96   0.050    -890.5645    .1402534
------------------------------------------------------------------------------

. nlcom atanh(/r)

       _nl_1: atanh(/r)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .3822957   .1990694     1.92   0.055    -.0078731    .7724644
------------------------------------------------------------------------------

. 
. ml clear

. ml model lf brentry_bioprobit_neg (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 =  rura
> l income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (hosp_f:cat_hosp2 = 
> cms_wage_index con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (ucc_
> v:cat_ucc = n_hospitals rural income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, 
> nocons) (ucc_f:cat_ucc = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3
>  /r if high_svi==0, constraints(1 2)

. eststo: ml max, iterate(100) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -775.33455
Rescale:      Log likelihood =  -641.8385
Rescale eq:   Log likelihood =  -439.2987
Iteration 0:  Log likelihood = -771.64313  (not concave)
Iteration 1:  Log likelihood = -512.04698  (not concave)
Iteration 2:  Log likelihood = -442.05853  (not concave)
Iteration 3:  Log likelihood = -432.76324  (not concave)
Iteration 4:  Log likelihood = -424.80521  (not concave)
Iteration 5:  Log likelihood = -416.82445  
Iteration 6:  Log likelihood = -395.67777  
Iteration 7:  Log likelihood = -393.10031  
Iteration 8:  Log likelihood =   -392.949  
Iteration 9:  Log likelihood = -392.94888  

                                                           Number of obs = 336
                                                           Wald chi2(0)  =   .
Log likelihood = -392.94888                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
--------------------------------------------------------------------------------
               | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
hosp_s         |
       tot_pop |          1  (constrained)
---------------+----------------------------------------------------------------
hosp_v         |
         rural |   99.49064   74.72992     1.33   0.183    -46.97732    245.9586
     income_pc |    21.0953   17.25738     1.22   0.222    -12.72855    54.91915
      hispanic |  -327.9921   95.56491    -3.43   0.001    -515.2958   -140.6883
 nonhisp_black |   1015.743   726.3261     1.40   0.162    -407.8297    2439.316
gte_highschool |  -572.5702   341.0271    -1.68   0.093    -1240.971    95.83057
        age_65 |   313.4358   291.2054     1.08   0.282    -257.3164     884.188
     uninsured |   223.1781   271.0404     0.82   0.410    -308.0513    754.4075
---------------+----------------------------------------------------------------
hosp_f         |
cms_wage_index |   .4520516   .8889825     0.51   0.611    -1.290322    2.194425
 con_intensity |   .8682228   .3621259     2.40   0.017      .158469    1.577977
---------------+----------------------------------------------------------------
      /hosp_a1 |   271.9826   142.1469     1.91   0.056    -6.620242    550.5855
      /hosp_g1 |   .8303314   .8582123     0.97   0.333    -.8517338    2.512397
---------------+----------------------------------------------------------------
ucc_s          |
      tot_pop2 |          1  (constrained)
---------------+----------------------------------------------------------------
ucc_v          |
   n_hospitals |  -36.15198   28.77591    -1.26   0.209    -92.55172    20.24776
         rural |   3.421942   69.48811     0.05   0.961    -132.7722    139.6161
     income_pc |  -11.18865   16.84805    -0.66   0.507    -44.21022    21.83292
      hispanic |  -273.1623   121.6285    -2.25   0.025    -511.5498   -34.77473
 nonhisp_black |  -596.7911    657.369    -0.91   0.364    -1885.211    691.6284
gte_highschool |  -321.2125   344.5377    -0.93   0.351     -996.494     354.069
        age_65 |  -182.9037   323.9568    -0.56   0.572    -817.8474    452.0399
     uninsured |   264.8238   379.4191     0.70   0.485    -478.8239    1008.472
---------------+----------------------------------------------------------------
ucc_f          |
cms_wage_index |   .7163528   .8129784     0.88   0.378    -.8770557    2.309761
---------------+----------------------------------------------------------------
       /ucc_a1 |   692.5543   161.7944     4.28   0.000     375.4432    1009.666
       /ucc_a2 |   242.8736    58.6126     4.14   0.000      127.995    357.7521
       /ucc_a3 |   40.03218   20.67273     1.94   0.053    -.4856291       80.55
       /ucc_g1 |   .8107389   .7870925     1.03   0.303    -.7319341    2.353412
       /ucc_g2 |   .1162999   .1635295     0.71   0.477     -.204212    .4368118
       /ucc_g3 |   .3115966   .1370477     2.27   0.023     .0429882    .5802051
            /r |   .2570479   .1571977     1.64   0.102    -.0510539    .5651497
--------------------------------------------------------------------------------
(est4 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. do "BR_EntryThreshold_Bi_svi.do" 10000 10000 "E" 0

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{    

. 
. 
end of do-file

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9270388   .7843829     1.18   0.237    -.6103233    2.464401
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.238635   .7901621     1.57   0.117    -.3100538    2.787325
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   449.6808   155.7118     2.89   0.004     144.4912    754.8704
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   409.6486   156.1922     2.62   0.009     103.5175    715.7797
------------------------------------------------------------------------------

. nlcom atanh(/r)

       _nl_1: atanh(/r)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .2629449   .1683191     1.56   0.118    -.0669545    .5928443
------------------------------------------------------------------------------

. 
. // Uninsured
. 
. ml clear

. ml model lf brentry_bioprobit_neg (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = rural
>  income_pc hispanic nonhisp_black gte_highschool age_65, nocons) (hosp_f:cat_hosp2 = cms_wage_in
> dex con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (ucc_v:cat_ucc =
>  n_hospitals rural income_pc hispanic nonhisp_black gte_highschool age_65, nocons) (ucc_f:cat_uc
> c = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3 /r if high_uninsured
> ==1, constraints(1 2)

. eststo: ml max, difficult iterate(100) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -774.24212
Rescale:      Log likelihood = -635.87314
Rescale eq:   Log likelihood = -389.31629
Iteration 0:  Log likelihood = -872.89347  (not concave)
Iteration 1:  Log likelihood = -706.28477  (not concave)
Iteration 2:  Log likelihood = -656.51315  (not concave)
Iteration 3:  Log likelihood = -629.08212  (not concave)
Iteration 4:  Log likelihood = -537.54066  (not concave)
Iteration 5:  Log likelihood = -456.78543  (not concave)
Iteration 6:  Log likelihood =  -425.0905  (not concave)
Iteration 7:  Log likelihood = -415.97322  (not concave)
Iteration 8:  Log likelihood =  -373.6656  
Iteration 9:  Log likelihood = -356.82717  
Iteration 10: Log likelihood = -355.25361  
Iteration 11: Log likelihood = -355.21994  
Iteration 12: Log likelihood = -355.21993  

                                                           Number of obs = 337
                                                           Wald chi2(0)  =   .
Log likelihood = -355.21993                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
--------------------------------------------------------------------------------
               | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
hosp_s         |
       tot_pop |          1  (constrained)
---------------+----------------------------------------------------------------
hosp_v         |
         rural |   150.0613   72.03085     2.08   0.037     8.883422    291.2392
     income_pc |  -13.98115   17.25909    -0.81   0.418    -47.80835    19.84604
      hispanic |  -57.43976   49.28718    -1.17   0.244    -154.0409    39.16134
 nonhisp_black |   539.9006   260.3995     2.07   0.038     29.52698    1050.274
gte_highschool |  -102.9158   242.6725    -0.42   0.671    -578.5452    372.7135
        age_65 |     163.93   205.0645     0.80   0.424    -237.9892    565.8491
---------------+----------------------------------------------------------------
hosp_f         |
cms_wage_index |    .195007    .828176     0.24   0.814    -1.428188    1.818202
 con_intensity |   .5769252   .4032826     1.43   0.153    -.2134941    1.367345
---------------+----------------------------------------------------------------
      /hosp_a1 |    164.046   110.0718     1.49   0.136    -51.69084    379.7828
      /hosp_g1 |   1.245918   .8045222     1.55   0.121    -.3309164    2.822752
---------------+----------------------------------------------------------------
ucc_s          |
      tot_pop2 |          1  (constrained)
---------------+----------------------------------------------------------------
ucc_v          |
   n_hospitals |  -44.50909   36.97329    -1.20   0.229    -116.9754    27.95723
         rural |  -3.598369   74.39758    -0.05   0.961    -149.4149    142.2182
     income_pc |  -57.68144   34.79107    -1.66   0.097    -125.8707     10.5078
      hispanic |   100.4623   81.25495     1.24   0.216    -58.79444    259.7191
 nonhisp_black |  -225.8779   272.3279    -0.83   0.407    -759.6308     307.875
gte_highschool |   1797.042   473.0185     3.80   0.000     869.9426    2724.141
        age_65 |   553.5254   270.1468     2.05   0.040     24.04728    1083.003
---------------+----------------------------------------------------------------
ucc_f          |
cms_wage_index |  -.0439609   .7239471    -0.06   0.952    -1.462871    1.374949
---------------+----------------------------------------------------------------
       /ucc_a1 |  -121.6099   202.3908    -0.60   0.548    -518.2885    275.0687
       /ucc_a2 |   336.7306   63.37765     5.31   0.000     212.5127    460.9485
       /ucc_a3 |  -10.88231   21.68723    -0.50   0.616    -53.38849    31.62388
       /ucc_g1 |   1.764846   .7158569     2.47   0.014     .3617922      3.1679
       /ucc_g2 |   .0391142    .166601     0.23   0.814    -.2874178    .3656462
       /ucc_g3 |   .5657131   .1819278     3.11   0.002     .2091411    .9222851
            /r |   .1183503    .170023     0.70   0.486    -.2148887    .4515893
--------------------------------------------------------------------------------
(est5 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. do "BR_EntryThreshold_Bi_Uninsured.do" 10000 10000 "F" 1

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{    

. 
end of do-file

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    1.80396   .7120275     2.53   0.011     .4084118    3.199508
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   2.369673   .7288818     3.25   0.001     .9410912    3.798255
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -458.3405   195.0625    -2.35   0.019    -840.6559   -76.02506
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -447.4582   197.7345    -2.26   0.024    -835.0107   -59.90562
------------------------------------------------------------------------------

. nlcom atanh(/r)

       _nl_1: atanh(/r)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .1189076   .1724383     0.69   0.490    -.2190654    .4568805
------------------------------------------------------------------------------

. 
. ml clear

. ml model lf brentry_bioprobit_neg (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = rural
>  income_pc hispanic nonhisp_black gte_highschool age_65, nocons) (hosp_f:cat_hosp2 = cms_wage_in
> dex con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (ucc_v:cat_ucc =
>  n_hospitals rural income_pc hispanic nonhisp_black gte_highschool age_65, nocons) (ucc_f:cat_uc
> c = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3 /r if high_uninsured
> ==0, constraints(1 2)

. eststo: ml max, difficult iterate(100) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -878.03705
Rescale:      Log likelihood =  -694.3832
Rescale eq:   Log likelihood = -459.98571
Iteration 0:  Log likelihood = -868.71941  (not concave)
Iteration 1:  Log likelihood = -546.16235  (not concave)
Iteration 2:  Log likelihood = -510.51515  (not concave)
Iteration 3:  Log likelihood = -475.19741  (not concave)
Iteration 4:  Log likelihood = -435.47683  
Iteration 5:  Log likelihood = -412.69169  
Iteration 6:  Log likelihood =  -408.8727  
Iteration 7:  Log likelihood = -408.64251  
Iteration 8:  Log likelihood = -408.64199  

                                                           Number of obs = 336
                                                           Wald chi2(0)  =   .
Log likelihood = -408.64199                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
--------------------------------------------------------------------------------
               | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
hosp_s         |
       tot_pop |          1  (constrained)
---------------+----------------------------------------------------------------
hosp_v         |
         rural |   100.7554   62.52068     1.61   0.107    -21.78292    223.2936
     income_pc |   20.34487   15.18009     1.34   0.180     -9.40756    50.09731
      hispanic |  -255.1489   74.43221    -3.43   0.001    -401.0333   -109.2644
 nonhisp_black |  -201.5533   864.5065    -0.23   0.816    -1895.955    1492.848
gte_highschool |  -621.1866   322.0123    -1.93   0.054    -1252.319    9.945857
        age_65 |   549.2772   280.1182     1.96   0.050     .2555737    1098.299
---------------+----------------------------------------------------------------
hosp_f         |
cms_wage_index |   1.185494    .723622     1.64   0.101    -.2327792    2.603767
 con_intensity |   .9266084   .3321526     2.79   0.005     .2756013    1.577615
---------------+----------------------------------------------------------------
      /hosp_a1 |   284.3909   132.2228     2.15   0.031     25.23886    543.5429
      /hosp_g1 |   .1271416   .7101808     0.18   0.858    -1.264787     1.51907
---------------+----------------------------------------------------------------
ucc_s          |
      tot_pop2 |          1  (constrained)
---------------+----------------------------------------------------------------
ucc_v          |
   n_hospitals |  -76.54735   24.53441    -3.12   0.002    -124.6339   -28.46079
         rural |   125.1767   57.74924     2.17   0.030     11.99028    238.3631
     income_pc |  -5.368256   14.47524    -0.37   0.711     -33.7392    23.00269
      hispanic |  -211.5231   81.47689    -2.60   0.009    -371.2149   -51.83134
 nonhisp_black |  -1255.843   789.3069    -1.59   0.112    -2802.856    291.1699
gte_highschool |  -351.2307   310.5249    -1.13   0.258    -959.8484    257.3869
        age_65 |  -44.24862   308.2595    -0.14   0.886    -648.4261    559.9288
---------------+----------------------------------------------------------------
ucc_f          |
cms_wage_index |  -.0232267   .5937511    -0.04   0.969    -1.186957    1.140504
---------------+----------------------------------------------------------------
       /ucc_a1 |   643.2049   137.7368     4.67   0.000     373.2457    913.1641
       /ucc_a2 |   208.8268    49.6847     4.20   0.000     111.4465     306.207
       /ucc_a3 |   9.462035   14.49334     0.65   0.514    -18.94439    37.86846
       /ucc_g1 |   1.506927   .6008195     2.51   0.012     .3293429    2.684512
       /ucc_g2 |   .0575549   .1467649     0.39   0.695     -.230099    .3452088
       /ucc_g3 |   .5309609    .139957     3.79   0.000     .2566502    .8052715
            /r |   .4205864   .1610943     2.61   0.009     .1048473    .7363255
--------------------------------------------------------------------------------
(est6 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. do "BR_EntryThreshold_Bi_Uninsured.do" 10000 10000 "G" 0

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{    

. 
end of do-file

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.564482   .5936163     2.64   0.008     .4010158    2.727949
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   2.095443    .601645     3.48   0.000     .9162407    3.274646
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   434.3781   130.6109     3.33   0.001     178.3855    690.3708
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   424.9161   131.5487     3.23   0.001     167.0854    682.7468
------------------------------------------------------------------------------

. nlcom atanh(/r)

       _nl_1: atanh(/r)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .4484043   .1957149     2.29   0.022       .06481    .8319985
------------------------------------------------------------------------------

. 
. estout *, drop(tot_pop*) cells(b(fmt(1)) se(fmt(1))) label mlabels("Low income" "High income" "H
> igh SVI" "Low SVI" "High uninsured" "Low uninsured")

--------------------------------------------------------------------------------------------------
                       Low income  High income     High SVI      Low SVI High unins~d Low uninsu~d
                             b/se         b/se         b/se         b/se         b/se         b/se
--------------------------------------------------------------------------------------------------
hosp_v                                                                                            
Rural                        79.1        147.8        117.0         99.5        150.1        100.8
                             70.0         63.7         60.5         74.7         72.0         62.5
Hispanic                    -86.5        -94.3        -63.7       -328.0        -57.4       -255.1
                             65.4         34.7         49.9         95.6         49.3         74.4
Black                       513.8        704.7        421.1       1015.7        539.9       -201.6
                            271.5        799.6        271.1        726.3        260.4        864.5
High school or more           1.5        -72.9       -214.0       -572.6       -102.9       -621.2
                            314.0        145.8        225.6        341.0        242.7        322.0
Age 65 or more              375.4        344.3        332.9        313.4        163.9        549.3
                            277.8        189.5        208.0        291.2        205.1        280.1
Uninsured                    50.9         72.5         -6.5        223.2                          
                            255.3        179.0        202.0        271.0                          
Income per capita                                       0.7         21.1        -14.0         20.3
                                                       13.2         17.3         17.3         15.2
--------------------------------------------------------------------------------------------------
hosp_f                                                                                            
CMS wage index                0.8          1.3          1.2          0.5          0.2          1.2
                              0.9          0.7          0.8          0.9          0.8          0.7
CON laws                      0.6          0.8          0.6          0.9          0.6          0.9
                              0.4          0.4          0.4          0.4          0.4          0.3
--------------------------------------------------------------------------------------------------
/                                                                                                 
hosp_a1                      88.9         69.9        157.1        272.0        164.0        284.4
                            162.2         81.2        100.2        142.1        110.1        132.2
hosp_g1                       0.9         -0.1          0.4          0.8          1.2          0.1
                              0.9          0.7          0.7          0.9          0.8          0.7
--------------------------------------------------------------------------------------------------
ucc_v                                                                                             
Additional hospita~e        -71.9        -57.4        -94.0        -36.2        -44.5        -76.5
                             34.6         25.8         31.3         28.8         37.0         24.5
Rural                       175.9         -4.3        129.0          3.4         -3.6        125.2
                             65.0         61.8         61.0         69.5         74.4         57.7
Hispanic                    110.3       -212.6         84.5       -273.2        100.5       -211.5
                             86.1         53.2         83.3        121.6         81.3         81.5
Black                      -147.2       -606.6       -159.2       -596.8       -225.9      -1255.8
                            265.7        707.1        269.3        657.4        272.3        789.3
High school or more        1377.1       -596.5       1223.9       -321.2       1797.0       -351.2
                            492.5        182.0        478.9        344.5        473.0        310.5
Age 65 or more              670.5       -110.1        636.4       -182.9        553.5        -44.2
                            312.6        257.7        281.8        324.0        270.1        308.3
Uninsured                   164.1        608.9        256.5        264.8                          
                            271.2        307.1        263.3        379.4                          
Income per capita                                      34.1        -11.2        -57.7         -5.4
                                                       37.0         16.8         34.8         14.5
--------------------------------------------------------------------------------------------------
ucc_f                                                                                             
CMS wage index                0.1          0.5         -0.1          0.7         -0.0         -0.0
                              0.7          0.6          0.6          0.8          0.7          0.6
--------------------------------------------------------------------------------------------------
/                                                                                                 
hosp_a1                      88.9         69.9        157.1        272.0        164.0        284.4
                            162.2         81.2        100.2        142.1        110.1        132.2
hosp_g1                       0.9         -0.1          0.4          0.8          1.2          0.1
                              0.9          0.7          0.7          0.9          0.8          0.7
--------------------------------------------------------------------------------------------------

. 
. ********************************************************************************
. // 3-type model
. ********************************************************************************
. 
. clear all

. use "PCSALevelData_v3.dta", clear

. capture program drop brentry_bioprobit_neg_3t

. qui do "brentry_bioprobit_negbin_3t_v2.do"

. 
. replace cat_aucc = og_n_hospaffucc_geo
(149 real changes made)

. replace cat_aucc = 2 if cat_aucc>2
(99 real changes made)

. replace n_hospaffucc_geo = cat_aucc
(149 real changes made)

. 
. constraint drop

. constraint 1 tot_pop = 1

. constraint 2 tot_pop2 = 1

. constraint 3 tot_pop3 = 1

. constraint 4 /r1 = /r2

. constraint 5 /r2 = /r3

. ml clear

. ml model lf brentry_bioprobit_neg_3t (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = ru
> ral income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (hosp_f:cat_hosp2 
> = cms_wage_index con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (uc
> c_v:cat_ucc = n_hospitals n_hospaffucc_geo rural income_pc hispanic nonhisp_black gte_highschool
>  age_65 uninsured, nocons) (ucc_f:cat_ucc = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc
> _g1 /ucc_g2 /ucc_g3 (aucc_s:cat_aucc = tot_pop3, nocons) (aucc_v:cat_aucc = n_hospitals n_urgent
> care rural income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (aucc_f:cat
> _aucc = cms_wage_index, nocons) /aucc_a1 /aucc_a2 /aucc_g1 /aucc_g2 /r1 /r2 /r3, constraints(1 2
>  3 4 5)

. eststo clear

. eststo: ml max, difficult iterate(300) tolerance(1e-3) ltolerance(1e-4)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -2169.7717
Rescale:      Log likelihood = -1866.3232
Rescale eq:   Log likelihood = -1386.5265
Iteration 0:  Log likelihood = -2078.0454  (not concave)
Iteration 1:  Log likelihood = -1622.3418  
Iteration 2:  Log likelihood = -1358.2156  (not concave)
Iteration 3:  Log likelihood = -1260.6779  
Iteration 4:  Log likelihood = -1257.0482  
Iteration 5:  Log likelihood = -1251.8367  
Iteration 6:  Log likelihood = -1251.2592  
Iteration 7:  Log likelihood =  -1251.252  

                                                           Number of obs = 673
                                                           Wald chi2(0)  =   .
Log likelihood = -1251.252                                 Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
 ( 3)  [aucc_s]tot_pop3 = 1
 ( 4)  [/]r1 - [/]r2 = 0
 ( 5)  [/]r2 - [/]r3 = 0
----------------------------------------------------------------------------------
                 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
hosp_s           |
         tot_pop |          1  (constrained)
-----------------+----------------------------------------------------------------
hosp_v           |
           rural |   119.1173   43.77708     2.72   0.007     33.31576    204.9188
       income_pc |  -1.422522   9.545658    -0.15   0.882    -20.13167    17.28662
        hispanic |  -84.63004   37.89469    -2.23   0.026    -158.9023   -10.35781
   nonhisp_black |   443.0038   236.8436     1.87   0.061    -21.20109    907.2088
  gte_highschool |  -139.2494   173.4961    -0.80   0.422    -479.2954    200.7966
          age_65 |   311.8793   157.9622     1.97   0.048     2.279038    621.4795
       uninsured |   56.87431   151.0393     0.38   0.707    -239.1573    352.9059
-----------------+----------------------------------------------------------------
hosp_f           |
  cms_wage_index |   .8575018   .5234977     1.64   0.101    -.1685349    1.883538
   con_intensity |   .6580876   .2333105     2.82   0.005     .2008075    1.115368
-----------------+----------------------------------------------------------------
        /hosp_a1 |   123.5784   74.62527     1.66   0.098    -22.68446    269.8412
        /hosp_g1 |   .4861805   .5112119     0.95   0.342    -.5157764    1.488137
-----------------+----------------------------------------------------------------
ucc_s            |
        tot_pop2 |          1  (constrained)
-----------------+----------------------------------------------------------------
ucc_v            |
     n_hospitals |  -62.83773   15.49579    -4.06   0.000    -93.20892   -32.46655
n_hospaffucc_geo |  -48.76672   7.586074    -6.43   0.000    -63.63515   -33.89829
           rural |   70.14883   41.49286     1.69   0.091    -11.17569    151.4733
       income_pc |  -24.89236   11.56077    -2.15   0.031    -47.55105   -2.233656
        hispanic |  -89.19142   50.76131    -1.76   0.079    -188.6818    10.29892
   nonhisp_black |  -250.1942   238.6255    -1.05   0.294    -717.8916    217.5032
  gte_highschool |   303.9141   234.0611     1.30   0.194    -154.8373    762.6655
          age_65 |   181.7512   182.5093     1.00   0.319    -175.9604    539.4628
       uninsured |   188.9075   187.4322     1.01   0.314    -178.4529    556.2679
-----------------+----------------------------------------------------------------
ucc_f            |
  cms_wage_index |   .3079589   .4488146     0.69   0.493    -.5717015    1.187619
-----------------+----------------------------------------------------------------
         /ucc_a1 |   399.5033   103.4257     3.86   0.000     196.7927     602.214
         /ucc_a2 |   211.7146   39.09397     5.42   0.000     135.0918    288.3374
         /ucc_a3 |    5.28686   10.33404     0.51   0.609    -14.96748     25.5412
         /ucc_g1 |   1.273866   .4479851     2.84   0.004     .3958317    2.151901
         /ucc_g2 |   .1551057   .1185162     1.31   0.191    -.0771819    .3873932
         /ucc_g3 |    .508223   .0989427     5.14   0.000     .3142989    .7021472
-----------------+----------------------------------------------------------------
aucc_s           |
        tot_pop3 |          1  (constrained)
-----------------+----------------------------------------------------------------
aucc_v           |
     n_hospitals |  -29.56087    10.0004    -2.96   0.003     -49.1613   -9.960437
    n_urgentcare |  -53.32468   8.515441    -6.26   0.000    -70.01463   -36.63472
           rural |   110.6215   35.91818     3.08   0.002     40.22313    181.0198
       income_pc |  -11.38219   6.500599    -1.75   0.080    -24.12313    1.358748
        hispanic |  -73.72794   21.65806    -3.40   0.001    -116.1769   -31.27893
   nonhisp_black |  -471.1177   245.6444    -1.92   0.055    -952.5719    10.33652
  gte_highschool |   150.3582   111.5446     1.35   0.178    -68.26523    368.9816
          age_65 |  -16.34521   103.0934    -0.16   0.874    -218.4045    185.7141
       uninsured |   218.5234   104.5354     2.09   0.037     13.63784    423.4089
-----------------+----------------------------------------------------------------
aucc_f           |
  cms_wage_index |  -.1965686   .4292875    -0.46   0.647    -1.037957    .6448194
-----------------+----------------------------------------------------------------
        /aucc_a1 |   207.3825   56.22733     3.69   0.000     97.17895    317.5861
        /aucc_a2 |  -.5631073   4.022525    -0.14   0.889    -8.447112    7.320898
        /aucc_g1 |   1.560992    .420637     3.71   0.000     .7365584    2.385425
        /aucc_g2 |   .4711891   .0661044     7.13   0.000     .3416268    .6007515
             /r1 |   .5146207   .0834125     6.17   0.000     .3511353    .6781061
             /r2 |   .5146207   .0834125     6.17   0.000     .3511353    .6781061
             /r3 |   .5146207   .0834125     6.17   0.000     .3511353    .6781061
----------------------------------------------------------------------------------
(est1 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. qui do "BR_EntryThreshold_Bi_3t.do" 10000 10000 10000 "I"

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.428972   .4444282     3.22   0.001     .5579087    2.300035
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.937195    .450382     4.30   0.000     1.054462    2.819928
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   187.7887   98.14541     1.91   0.056    -4.572744    380.1502
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   182.5019   98.93661     1.84   0.065    -11.41033    376.4141
------------------------------------------------------------------------------

. nlcom atanh(/r1)

       _nl_1: atanh(/r1)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .5689949   .1134608     5.01   0.000     .3466158    .7913739
------------------------------------------------------------------------------

. 
. do "BR_EntryThreshold_Bi_3t_cond.do" 10000 10000 10000 0 0

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{

. 
. disp "UCC"
UCC

. matrix list t_monopoly

symmetric t_monopoly[1,1]
        _cons
r1  31.239923

. matrix list se_monopoly

symmetric se_monopoly[1,1]
           c1
r1  1.7078917

. matrix list t_duopoly

symmetric t_duopoly[1,1]
        _cons
r1  29.881609

. matrix list se_duopoly

symmetric se_duopoly[1,1]
           c1
r1  1.8022197

. matrix list t_nfirms3

symmetric t_nfirms3[1,1]
        _cons
r1  26.816085

. matrix list se_f3

symmetric se_f3[1,1]
           c1
r1  1.5846412

. matrix list t_2f_1f

symmetric t_2f_1f[1,1]
           c1
r1  .95651994

. matrix list se_t21

symmetric se_t21[1,1]
           c1
r1  .05501823

. matrix list t_3f_2f

symmetric t_3f_2f[1,1]
           c1
r1  .89741102

. matrix list se_t32

symmetric se_t32[1,1]
           c1
r1  .02889033

. 
end of do-file

. do "BR_EntryThreshold_Bi_3t_cond.do" 10000 10000 10000 1 0

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{

. 
. disp "UCC"
UCC

. matrix list t_monopoly

symmetric t_monopoly[1,1]
        _cons
r1  35.723441

. matrix list se_monopoly

symmetric se_monopoly[1,1]
           c1
r1  2.4055267

. matrix list t_duopoly

symmetric t_duopoly[1,1]
        _cons
r1  38.232401

. matrix list se_duopoly

symmetric se_duopoly[1,1]
          c1
r1  2.714926

. matrix list t_nfirms3

symmetric t_nfirms3[1,1]
        _cons
r1  34.679252

. matrix list se_f3

symmetric se_f3[1,1]
          c1
r1  2.315946

. matrix list t_2f_1f

symmetric t_2f_1f[1,1]
           c1
r1  1.0702329

. matrix list se_t21

symmetric se_t21[1,1]
           c1
r1  .07059633

. matrix list t_3f_2f

symmetric t_3f_2f[1,1]
           c1
r1  .90706445

. matrix list se_t32

symmetric se_t32[1,1]
           c1
r1  .03055106

. 
end of do-file

. do "BR_EntryThreshold_Bi_3t_cond.do" 10000 10000 10000 1 1

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{

. 
. disp "UCC"
UCC

. matrix list t_monopoly

symmetric t_monopoly[1,1]
        _cons
r1  40.201088

. matrix list se_monopoly

symmetric se_monopoly[1,1]
           c1
r1  3.0319812

. matrix list t_duopoly

symmetric t_duopoly[1,1]
       _cons
r1  48.82084

. matrix list se_duopoly

symmetric se_duopoly[1,1]
           c1
r1  3.5013185

. matrix list t_nfirms3

symmetric t_nfirms3[1,1]
        _cons
r1  44.896002

. matrix list se_f3

symmetric se_f3[1,1]
           c1
r1  2.7220733

. matrix list t_2f_1f

symmetric t_2f_1f[1,1]
           c1
r1  1.2144159

. matrix list se_t21

symmetric se_t21[1,1]
           c1
r1  .09398308

. matrix list t_3f_2f

symmetric t_3f_2f[1,1]
           c1
r1  .91960734

. matrix list se_t32

symmetric se_t32[1,1]
          c1
r1  .0365768

. 
end of do-file

. do "BR_EntryThreshold_Bi_3t_cond.do" 10000 10000 10000 1 2

. ***************************************************
. * Calculation of Bresnahan-Reiss Entry Thresholds *
. ***************************************************
. 
. qui{

. 
. disp "UCC"
UCC

. matrix list t_monopoly

symmetric t_monopoly[1,1]
        _cons
r1  45.962064

. matrix list se_monopoly

symmetric se_monopoly[1,1]
           c1
r1  4.2447907

. matrix list t_duopoly

symmetric t_duopoly[1,1]
        _cons
r1  67.520637

. matrix list se_duopoly

symmetric se_duopoly[1,1]
           c1
r1  7.0915596

. matrix list t_nfirms3

symmetric t_nfirms3[1,1]
        _cons
r1  63.646794

. matrix list se_f3

symmetric se_f3[1,1]
           c1
r1  5.7488825

. matrix list t_2f_1f

symmetric t_2f_1f[1,1]
           c1
r1  1.4690515

. matrix list se_t21

symmetric se_t21[1,1]
           c1
r1  .15981613

. matrix list t_3f_2f

symmetric t_3f_2f[1,1]
           c1
r1  .94262728

. matrix list se_t32

symmetric se_t32[1,1]
           c1
r1  .05390825

. 
end of do-file

. 
. estout *, drop(tot_pop*) cells("b(fmt(1)) se(fmt(1))") label mlabels("3-type")

----------------------------------------------
                           3-type             
                                b           se
----------------------------------------------
hosp_v                                        
Rural                       119.1         43.8
Income per capita            -1.4          9.5
Hispanic                    -84.6         37.9
Black                       443.0        236.8
High school or more        -139.2        173.5
Age 65 or more              311.9        158.0
Uninsured                    56.9        151.0
----------------------------------------------
hosp_f                                        
CMS wage index                0.9          0.5
CON laws                      0.7          0.2
----------------------------------------------
/                                             
hosp_a1                     123.6         74.6
hosp_g1                       0.5          0.5
----------------------------------------------
ucc_v                                         
Additional hospita~e        -62.8         15.5
Number of AUCCs             -48.8          7.6
Rural                        70.1         41.5
Income per capita           -24.9         11.6
Hispanic                    -89.2         50.8
Black                      -250.2        238.6
High school or more         303.9        234.1
Age 65 or more              181.8        182.5
Uninsured                   188.9        187.4
----------------------------------------------
ucc_f                                         
CMS wage index                0.3          0.4
----------------------------------------------
/                                             
ucc_a1                      399.5        103.4
ucc_a2                      211.7         39.1
ucc_a3                        5.3         10.3
ucc_g1                        1.3          0.4
ucc_g2                        0.2          0.1
ucc_g3                        0.5          0.1
----------------------------------------------
aucc_v                                        
Additional hospita~e        -29.6         10.0
Number of UCCs              -53.3          8.5
Rural                       110.6         35.9
Income per capita           -11.4          6.5
Hispanic                    -73.7         21.7
Black                      -471.1        245.6
High school or more         150.4        111.5
Age 65 or more              -16.3        103.1
Uninsured                   218.5        104.5
----------------------------------------------
aucc_f                                        
CMS wage index               -0.2          0.4
----------------------------------------------
/                                             
aucc_a1                     207.4         56.2
aucc_a2                      -0.6          4.0
aucc_g1                       1.6          0.4
aucc_g2                       0.5          0.1
r1                            0.5          0.1
r2                            0.5          0.1
r3                            0.5          0.1
----------------------------------------------

. 
. // 3-type model in subsamples
. 
. * Income
. 
. ml clear

. ml model lf brentry_bioprobit_neg_3t (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = ru
> ral hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (hosp_f:cat_hosp2 = cms_wage
> _index con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (ucc_v:cat_uc
> c = n_hospitals n_hospaffucc_geo rural hispanic nonhisp_black gte_highschool age_65 uninsured, n
> ocons) (ucc_f:cat_ucc = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3 
> (aucc_s:cat_aucc = tot_pop3, nocons) (aucc_v:cat_aucc = n_hospitals n_urgentcare rural hispanic 
> nonhisp_black gte_highschool age_65 uninsured, nocons) (aucc_f:cat_aucc = cms_wage_index, nocons
> ) /aucc_a1 /aucc_a2 /aucc_g1 /aucc_g2 /r1 /r2 /r3 if high_income==0, constraints(1 2 3 4 5) tech
> nique(bfgs)

. eststo clear

. eststo: ml max, difficult iterate(300) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -937.98481
Rescale:      Log likelihood = -872.37314
Rescale eq:   Log likelihood = -674.60845
Iteration 0:  Log likelihood = -982.78887  
Iteration 1:  Log likelihood = -956.13128  (backed up)
Iteration 2:  Log likelihood = -947.09379  (backed up)
Iteration 3:  Log likelihood = -921.73995  (backed up)
Iteration 4:  Log likelihood = -916.87946  (backed up)
Iteration 5:  Log likelihood = -913.65119  (backed up)
Iteration 6:  Log likelihood = -909.96593  (backed up)
Iteration 7:  Log likelihood = -903.95151  (backed up)
Iteration 8:  Log likelihood =  -874.5513  
Iteration 9:  Log likelihood = -842.06719  
Iteration 10: Log likelihood = -809.49343  
Iteration 11: Log likelihood = -797.94949  
Iteration 12: Log likelihood = -787.24817  
Iteration 13: Log likelihood = -779.32551  
Iteration 14: Log likelihood = -765.84286  
Iteration 15: Log likelihood =  -750.8686  
Iteration 16: Log likelihood = -739.30274  
Iteration 17: Log likelihood =  -730.6648  
Iteration 18: Log likelihood = -715.14679  
Iteration 19: Log likelihood = -708.23417  
Iteration 20: Log likelihood = -703.19829  
Iteration 21: Log likelihood =  -700.3997  
Iteration 22: Log likelihood = -698.21122  
Iteration 23: Log likelihood = -692.41203  
Iteration 24: Log likelihood = -683.49488  
Iteration 25: Log likelihood = -673.26822  
Iteration 26: Log likelihood =   -669.711  
Iteration 27: Log likelihood = -668.59092  
Iteration 28: Log likelihood = -667.21164  
Iteration 29: Log likelihood = -666.11892  
Iteration 30: Log likelihood = -663.99378  
Iteration 31: Log likelihood = -657.06441  
Iteration 32: Log likelihood = -644.29559  
Iteration 33: Log likelihood = -625.55188  
Iteration 34: Log likelihood = -619.73627  
Iteration 35: Log likelihood = -614.25995  
Iteration 36: Log likelihood = -611.55534  
Iteration 37: Log likelihood = -609.89121  
Iteration 38: Log likelihood = -609.45461  
Iteration 39: Log likelihood = -606.69841  
Iteration 40: Log likelihood = -604.56908  
Iteration 41: Log likelihood =  -603.7631  
Iteration 42: Log likelihood = -603.18684  
Iteration 43: Log likelihood = -601.49024  
Iteration 44: Log likelihood =  -599.5167  
Iteration 45: Log likelihood = -599.07047  
Iteration 46: Log likelihood = -598.93193  
Iteration 47: Log likelihood = -598.64889  
Iteration 48: Log likelihood = -597.49471  
Iteration 49: Log likelihood = -592.41135  
Iteration 50: Log likelihood = -587.92585  
Iteration 51: Log likelihood =  -587.2157  
Iteration 52: Log likelihood = -587.14078  
Iteration 53: Log likelihood = -587.12807  
Iteration 54: Log likelihood = -587.09749  
Iteration 55: Log likelihood = -586.85938  
Iteration 56: Log likelihood = -586.02696  
Iteration 57: Log likelihood = -585.96406  
Iteration 58: Log likelihood =  -585.9553  
Iteration 59: Log likelihood = -585.94289  
Iteration 60: Log likelihood = -585.70448  
Iteration 61: Log likelihood = -584.96395  
Iteration 62: Log likelihood = -584.68897  
Iteration 63: Log likelihood =  -584.6753  
Iteration 64: Log likelihood = -584.67253  
Iteration 65: Log likelihood = -584.62393  
Iteration 66: Log likelihood = -584.07973  
Iteration 67: Log likelihood = -583.95425  
Iteration 68: Log likelihood = -583.94828  
Iteration 69: Log likelihood = -583.94132  
Iteration 70: Log likelihood = -583.82312  
Iteration 71: Log likelihood = -583.21179  
Iteration 72: Log likelihood = -582.81161  
Iteration 73: Log likelihood = -582.80557  
Iteration 74: Log likelihood = -582.79537  
Iteration 75: Log likelihood = -582.41112  
Iteration 76: Log likelihood = -581.86535  
Iteration 77: Log likelihood = -581.82229  
Iteration 78: Log likelihood = -581.81301  
Iteration 79: Log likelihood = -581.53546  
Iteration 80: Log likelihood = -580.23843  
Iteration 81: Log likelihood = -580.15844  
Iteration 82: Log likelihood = -580.15307  
Iteration 83: Log likelihood = -579.99945  
Iteration 84: Log likelihood =  -578.9864  
Iteration 85: Log likelihood = -577.96126  
Iteration 86: Log likelihood = -577.81845  
Iteration 87: Log likelihood = -577.81721  
Iteration 88: Log likelihood = -577.81621  
Iteration 89: Log likelihood = -577.76168  
Iteration 90: Log likelihood = -577.30973  
Iteration 91: Log likelihood = -577.24077  
Iteration 92: Log likelihood =    -577.24  
Iteration 93: Log likelihood = -577.23736  
Iteration 94: Log likelihood = -576.58557  
Iteration 95: Log likelihood = -575.21469  
Iteration 96: Log likelihood =  -575.1623  
Iteration 97: Log likelihood = -575.16206  
Iteration 98: Log likelihood = -575.15754  
Iteration 99: Log likelihood = -575.00027  
Iteration 100: Log likelihood = -574.94841  
Iteration 101: Log likelihood = -574.94772  
Iteration 102: Log likelihood = -574.94711  
Iteration 103: Log likelihood = -574.92436  
Iteration 104: Log likelihood = -574.82492  
Iteration 105: Log likelihood = -574.79714  
Iteration 106: Log likelihood = -574.79693  
Iteration 107: Log likelihood = -574.79625  
Iteration 108: Log likelihood = -574.69257  
Iteration 109: Log likelihood = -574.35118  
Iteration 110: Log likelihood = -574.22217  
Iteration 111: Log likelihood = -574.21781  
Iteration 112: Log likelihood = -574.21766  
Iteration 113: Log likelihood = -574.21565  
Iteration 114: Log likelihood = -573.94651  
Iteration 115: Log likelihood = -573.88473  
Iteration 116: Log likelihood = -573.88355  
Iteration 117: Log likelihood = -573.88354  
Iteration 118: Log likelihood = -573.88346  
Iteration 119: Log likelihood =  -573.8822  
Iteration 120: Log likelihood = -573.51576  
Iteration 121: Log likelihood = -573.48675  
Iteration 122: Log likelihood =  -573.4865  
Iteration 123: Log likelihood =  -573.4865  
Iteration 124: Log likelihood = -573.48645  
Iteration 125: Log likelihood = -573.48559  
Iteration 126: Log likelihood = -573.46017  
Iteration 127: Log likelihood = -573.41607  
Iteration 128: Log likelihood = -573.41569  
Iteration 129: Log likelihood = -573.41569  
Iteration 130: Log likelihood = -573.41568  
Iteration 131: Log likelihood = -573.41556  
Iteration 132: Log likelihood = -573.41199  
Iteration 133: Log likelihood = -573.40786  
Iteration 134: Log likelihood = -573.40775  
Iteration 135: Log likelihood = -573.40774  
Iteration 136: Log likelihood = -573.40774  
Iteration 137: Log likelihood = -573.40774  
Iteration 138: Log likelihood = -573.40675  
Iteration 139: Log likelihood = -573.40583  
Iteration 140: Log likelihood = -573.40579  
Iteration 141: Log likelihood = -573.40578  
Iteration 142: Log likelihood = -573.40578  
Iteration 143: Log likelihood = -573.40578  
Iteration 144: Log likelihood = -573.40578  
Iteration 145: Log likelihood =  -573.4057  
Iteration 146: Log likelihood = -573.40568  

                                                           Number of obs = 337
                                                           Wald chi2(0)  =   .
Log likelihood = -573.40568                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
 ( 3)  [aucc_s]tot_pop3 = 1
 ( 4)  [/]r1 - [/]r2 = 0
 ( 5)  [/]r2 - [/]r3 = 0
----------------------------------------------------------------------------------
                 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
hosp_s           |
         tot_pop |          1  (constrained)
-----------------+----------------------------------------------------------------
hosp_v           |
           rural |   92.28661   66.08269     1.40   0.163    -37.23308    221.8063
        hispanic |  -68.45492   64.30672    -1.06   0.287    -194.4938    57.58393
   nonhisp_black |   508.8815   267.3524     1.90   0.057    -15.11958    1032.882
  gte_highschool |  -23.65713   302.1694    -0.08   0.938    -615.8982    568.5839
          age_65 |   365.9922   275.1591     1.33   0.183    -173.3096    905.2941
       uninsured |  -11.71437   261.8379    -0.04   0.964    -524.9073    501.4786
-----------------+----------------------------------------------------------------
hosp_f           |
  cms_wage_index |   .7292315   .8924975     0.82   0.414    -1.020031    2.478494
   con_intensity |   .4727208   .3480701     1.36   0.174     -.209484    1.154926
-----------------+----------------------------------------------------------------
        /hosp_a1 |   94.22801   152.6687     0.62   0.537    -204.9972    393.4532
        /hosp_g1 |   .8814605   .8578254     1.03   0.304    -.7998464    2.562767
-----------------+----------------------------------------------------------------
ucc_s            |
        tot_pop2 |          1  (constrained)
-----------------+----------------------------------------------------------------
ucc_v            |
     n_hospitals |  -95.87862   27.80499    -3.45   0.001    -150.3754   -41.38184
n_hospaffucc_geo |  -82.89204   14.50138    -5.72   0.000    -111.3142   -54.46985
           rural |   146.3945   63.25238     2.31   0.021     22.42207    270.3668
        hispanic |   42.97251   80.45282     0.53   0.593    -114.7121    200.6571
   nonhisp_black |  -237.9504   266.0612    -0.89   0.371    -759.4207    283.5199
  gte_highschool |    1374.24   491.0764     2.80   0.005     411.7479    2336.732
          age_65 |   544.5162   304.8753     1.79   0.074    -53.02841    1142.061
       uninsured |   335.4885   286.0169     1.17   0.241    -225.0943    896.0712
-----------------+----------------------------------------------------------------
ucc_f            |
  cms_wage_index |  -.4164328   .6817795    -0.61   0.541    -1.752696    .9198304
-----------------+----------------------------------------------------------------
         /ucc_a1 |  -91.33969   232.6506    -0.39   0.695    -547.3266    364.6472
         /ucc_a2 |   234.5574   62.97936     3.72   0.000     111.1201    357.9947
         /ucc_a3 |  -4.440359   23.08394    -0.19   0.847    -49.68405    40.80333
         /ucc_g1 |    2.33373   .6838085     3.41   0.001     .9934898     3.67397
         /ucc_g2 |   .2445791   .2014281     1.21   0.225    -.1502127     .639371
         /ucc_g3 |   .5413201   .1810421     2.99   0.003     .1864842     .896156
-----------------+----------------------------------------------------------------
aucc_s           |
        tot_pop3 |          1  (constrained)
-----------------+----------------------------------------------------------------
aucc_v           |
     n_hospitals |  -73.52869   20.11291    -3.66   0.000    -112.9493   -34.10811
    n_urgentcare |  -58.42044   13.19608    -4.43   0.000    -84.28428    -32.5566
           rural |   112.2582   58.15331     1.93   0.054    -1.720227    226.2366
        hispanic |  -69.27658   49.24556    -1.41   0.159    -165.7961    27.24294
   nonhisp_black |  -814.6157   366.5941    -2.22   0.026    -1533.127   -96.10444
  gte_highschool |   387.3743   263.0385     1.47   0.141    -128.1717    902.9204
          age_65 |   266.7527   211.4236     1.26   0.207    -147.6301    681.1354
       uninsured |   344.2633   169.8748     2.03   0.043      11.3149    677.2117
-----------------+----------------------------------------------------------------
aucc_f           |
  cms_wage_index |  -1.364269   .6751721    -2.02   0.043    -2.687582   -.0409555
-----------------+----------------------------------------------------------------
        /aucc_a1 |   101.7049     127.53     0.80   0.425    -148.2493     351.659
        /aucc_a2 |   7.605866   9.853524     0.77   0.440    -11.70669    26.91842
        /aucc_g1 |   2.950643   .6666214     4.43   0.000     1.644089    4.257197
        /aucc_g2 |   .6119839   .1213501     5.04   0.000      .374142    .8498258
             /r1 |   .5362298   .1339681     4.00   0.000     .2736573    .7988024
             /r2 |   .5362298   .1339681     4.00   0.000     .2736573    .7988024
             /r3 |   .5362298   .1339681     4.00   0.000     .2736573    .7988024
----------------------------------------------------------------------------------
(est1 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. qui do "BR_EntryThreshold_Bi_3t_Income.do" 10000 10000 10000 "J" 0

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   2.578309    .670255     3.85   0.000     1.264633    3.891985
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   3.119629    .682805     4.57   0.000     1.781356    4.457902
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -325.8971   230.9679    -1.41   0.158    -778.5858    126.7916
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -321.4567   233.9366    -1.37   0.169     -779.964    137.0506
------------------------------------------------------------------------------

. nlcom atanh(/r1)

       _nl_1: atanh(/r1)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .5988487   .1880365     3.18   0.001     .2303039    .9673936
------------------------------------------------------------------------------

. 
. ml clear

. ml model lf brentry_bioprobit_neg_3t (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = ru
> ral hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (hosp_f:cat_hosp2 = cms_wage
> _index con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (ucc_v:cat_uc
> c = n_hospitals n_hospaffucc_geo rural hispanic nonhisp_black gte_highschool age_65 uninsured, n
> ocons) (ucc_f:cat_ucc = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3 
> (aucc_s:cat_aucc = tot_pop3, nocons) (aucc_v:cat_aucc = n_hospitals n_urgentcare rural hispanic 
> nonhisp_black gte_highschool age_65 uninsured, nocons) (aucc_f:cat_aucc = cms_wage_index, nocons
> ) /aucc_a1 /aucc_a2 /aucc_g1 /aucc_g2 /r1 /r2 /r3 if high_income==1, constraints(1 2 3 4 5) tech
> nique(bfgs)

. eststo: ml max, difficult iterate(300) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood =  -1229.668
Rescale:      Log likelihood = -989.57715
Rescale eq:   Log likelihood = -697.39199
Iteration 0:  Log likelihood = -1277.1341  
Iteration 1:  Log likelihood = -1123.3053  (backed up)
Iteration 2:  Log likelihood = -927.40819  (backed up)
Iteration 3:  Log likelihood = -910.17182  (backed up)
Iteration 4:  Log likelihood = -907.03698  (backed up)
Iteration 5:  Log likelihood = -906.24022  (backed up)
Iteration 6:  Log likelihood = -905.68189  (backed up)
Iteration 7:  Log likelihood = -905.61459  (backed up)
Iteration 8:  Log likelihood = -892.77514  
Iteration 9:  Log likelihood = -849.99549  
Iteration 10: Log likelihood = -818.17196  
Iteration 11: Log likelihood = -798.59176  
Iteration 12: Log likelihood = -786.03151  
Iteration 13: Log likelihood = -770.47682  
Iteration 14: Log likelihood = -754.50481  
Iteration 15: Log likelihood = -744.77665  
Iteration 16: Log likelihood = -737.31984  
Iteration 17: Log likelihood = -736.40154  
Iteration 18: Log likelihood = -732.98563  
Iteration 19: Log likelihood = -729.71366  
Iteration 20: Log likelihood = -725.11473  
Iteration 21: Log likelihood = -724.70981  
Iteration 22: Log likelihood = -721.24483  
Iteration 23: Log likelihood = -718.66705  
Iteration 24: Log likelihood = -715.90413  
Iteration 25: Log likelihood = -712.17245  
Iteration 26: Log likelihood = -709.52198  
Iteration 27: Log likelihood = -707.79424  
Iteration 28: Log likelihood = -706.59777  
Iteration 29: Log likelihood = -704.11799  
Iteration 30: Log likelihood = -695.50455  
Iteration 31: Log likelihood = -688.34645  
Iteration 32: Log likelihood = -685.01772  
Iteration 33: Log likelihood = -684.49859  
Iteration 34: Log likelihood = -683.70538  
Iteration 35: Log likelihood = -682.40006  
Iteration 36: Log likelihood = -679.46254  
Iteration 37: Log likelihood = -670.06548  
Iteration 38: Log likelihood = -665.55724  
Iteration 39: Log likelihood = -664.76992  
Iteration 40: Log likelihood =  -663.3351  
Iteration 41: Log likelihood =  -659.8955  
Iteration 42: Log likelihood = -657.46166  
Iteration 43: Log likelihood =  -657.0606  
Iteration 44: Log likelihood = -656.98493  
Iteration 45: Log likelihood = -656.79798  
Iteration 46: Log likelihood = -656.01552  
Iteration 47: Log likelihood = -654.11869  
Iteration 48: Log likelihood = -652.80511  
Iteration 49: Log likelihood = -652.61327  
Iteration 50: Log likelihood = -652.54646  
Iteration 51: Log likelihood = -652.22261  
Iteration 52: Log likelihood = -650.77454  
Iteration 53: Log likelihood =   -648.758  
Iteration 54: Log likelihood = -648.64943  
Iteration 55: Log likelihood = -648.63976  
Iteration 56: Log likelihood = -648.63126  
Iteration 57: Log likelihood = -648.53475  
Iteration 58: Log likelihood =  -647.7518  
Iteration 59: Log likelihood = -647.22122  
Iteration 60: Log likelihood = -647.19619  
Iteration 61: Log likelihood = -647.19246  
Iteration 62: Log likelihood = -647.16804  
Iteration 63: Log likelihood = -646.79553  
Iteration 64: Log likelihood = -645.12096  
Iteration 65: Log likelihood = -644.67428  
Iteration 66: Log likelihood = -644.65948  
Iteration 67: Log likelihood =  -644.6568  
Iteration 68: Log likelihood = -644.62334  
Iteration 69: Log likelihood = -643.13534  
Iteration 70: Log likelihood = -642.09508  
Iteration 71: Log likelihood = -641.94509  
Iteration 72: Log likelihood = -641.93776  
Iteration 73: Log likelihood =  -641.9257  
Iteration 74: Log likelihood = -641.68993  
Iteration 75: Log likelihood = -640.56186  
Iteration 76: Log likelihood = -640.42175  
Iteration 77: Log likelihood = -640.41411  
Iteration 78: Log likelihood = -640.40949  
Iteration 79: Log likelihood = -640.18141  
Iteration 80: Log likelihood = -639.58541  
Iteration 81: Log likelihood = -639.52964  
Iteration 82: Log likelihood = -639.52477  
Iteration 83: Log likelihood = -639.52355  
Iteration 84: Log likelihood = -639.51504  
Iteration 85: Log likelihood = -639.41948  
Iteration 86: Log likelihood = -638.91836  
Iteration 87: Log likelihood = -638.76162  
Iteration 88: Log likelihood = -638.75915  
Iteration 89: Log likelihood =  -638.7567  
Iteration 90: Log likelihood = -638.59297  
Iteration 91: Log likelihood =  -637.9899  
Iteration 92: Log likelihood = -637.96402  
Iteration 93: Log likelihood = -637.96314  
Iteration 94: Log likelihood = -637.95893  
Iteration 95: Log likelihood = -637.86063  
Iteration 96: Log likelihood = -637.53817  
Iteration 97: Log likelihood = -637.39546  
Iteration 98: Log likelihood = -637.37968  
Iteration 99: Log likelihood =  -637.3753  
Iteration 100: Log likelihood = -637.37506  
Iteration 101: Log likelihood = -637.37401  
Iteration 102: Log likelihood = -637.36219  
Iteration 103: Log likelihood = -637.19844  
Iteration 104: Log likelihood = -637.12967  
Iteration 105: Log likelihood = -637.12492  
Iteration 106: Log likelihood = -637.12473  
Iteration 107: Log likelihood = -637.12456  
Iteration 108: Log likelihood = -637.12223  
Iteration 109: Log likelihood = -637.07721  
Iteration 110: Log likelihood = -637.04874  
Iteration 111: Log likelihood = -637.04746  
Iteration 112: Log likelihood = -637.04739  
Iteration 113: Log likelihood = -637.04625  
Iteration 114: Log likelihood =  -637.0331  
Iteration 115: Log likelihood = -636.89245  
Iteration 116: Log likelihood = -636.84459  
Iteration 117: Log likelihood = -636.84347  
Iteration 118: Log likelihood = -636.84342  
Iteration 119: Log likelihood = -636.84332  
Iteration 120: Log likelihood = -636.84225  
Iteration 121: Log likelihood = -636.77395  
Iteration 122: Log likelihood = -636.63902  
Iteration 123: Log likelihood = -636.63614  
Iteration 124: Log likelihood = -636.63611  
Iteration 125: Log likelihood = -636.63604  
Iteration 126: Log likelihood = -636.63099  
Iteration 127: Log likelihood = -636.57343  
Iteration 128: Log likelihood = -636.47692  
Iteration 129: Log likelihood = -636.46596  
Iteration 130: Log likelihood = -636.46591  
Iteration 131: Log likelihood = -636.46586  
Iteration 132: Log likelihood = -636.46499  
Iteration 133: Log likelihood = -636.31849  
Iteration 134: Log likelihood =  -636.2735  
Iteration 135: Log likelihood = -636.27239  
Iteration 136: Log likelihood = -636.27238  
Iteration 137: Log likelihood = -636.27237  
Iteration 138: Log likelihood = -636.27206  
Iteration 139: Log likelihood = -636.25645  
Iteration 140: Log likelihood = -636.25389  
Iteration 141: Log likelihood = -636.25388  
Iteration 142: Log likelihood = -636.25388  
Iteration 143: Log likelihood = -636.25386  
Iteration 144: Log likelihood = -636.25368  
Iteration 145: Log likelihood = -636.25188  
Iteration 146: Log likelihood = -636.24528  
Iteration 147: Log likelihood = -636.24527  
Iteration 148: Log likelihood = -636.24526  
Iteration 149: Log likelihood = -636.24511  
Iteration 150: Log likelihood = -636.24216  
Iteration 151: Log likelihood = -636.23935  
Iteration 152: Log likelihood = -636.23929  
Iteration 153: Log likelihood = -636.23929  
Iteration 154: Log likelihood = -636.23929  
Iteration 155: Log likelihood =  -636.2392  
Iteration 156: Log likelihood = -636.23542  
Iteration 157: Log likelihood = -636.23509  
Iteration 158: Log likelihood = -636.23509  
Iteration 159: Log likelihood = -636.23509  
Iteration 160: Log likelihood = -636.23508  
Iteration 161: Log likelihood = -636.23497  
Iteration 162: Log likelihood = -636.23445  
Iteration 163: Log likelihood = -636.23439  
Iteration 164: Log likelihood = -636.23439  
Iteration 165: Log likelihood = -636.23439  
Iteration 166: Log likelihood = -636.23425  
Iteration 167: Log likelihood = -636.23349  
Iteration 168: Log likelihood = -636.23316  
Iteration 169: Log likelihood = -636.23316  

                                                           Number of obs = 336
                                                           Wald chi2(0)  =   .
Log likelihood = -636.23316                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
 ( 3)  [aucc_s]tot_pop3 = 1
 ( 4)  [/]r1 - [/]r2 = 0
 ( 5)  [/]r2 - [/]r3 = 0
----------------------------------------------------------------------------------
                 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
hosp_s           |
         tot_pop |          1  (constrained)
-----------------+----------------------------------------------------------------
hosp_v           |
           rural |   140.1256   61.22697     2.29   0.022     20.12299    260.1283
        hispanic |  -86.55585   37.69714    -2.30   0.022    -160.4409   -12.67081
   nonhisp_black |   758.6312     750.68     1.01   0.312    -712.6745    2229.937
  gte_highschool |  -40.65839    162.229    -0.25   0.802    -358.6214    277.3046
          age_65 |   277.9594   191.0468     1.45   0.146    -96.48542    652.4042
       uninsured |   38.37081   173.7459     0.22   0.825    -302.1649    378.9065
-----------------+----------------------------------------------------------------
hosp_f           |
  cms_wage_index |   1.235845    .675852     1.83   0.067    -.0888008     2.56049
   con_intensity |    .807728   .3335722     2.42   0.015     .1539385    1.461517
-----------------+----------------------------------------------------------------
        /hosp_a1 |   64.46764   88.63157     0.73   0.467    -109.2471    238.1823
        /hosp_g1 |  -.1131849   .6678857    -0.17   0.865    -1.422217    1.195847
-----------------+----------------------------------------------------------------
ucc_s            |
        tot_pop2 |          1  (constrained)
-----------------+----------------------------------------------------------------
ucc_v            |
     n_hospitals |  -43.41154   20.54747    -2.11   0.035    -83.68385   -3.139236
n_hospaffucc_geo |  -35.10378    9.30407    -3.77   0.000    -53.33942   -16.86813
           rural |   3.766172   59.94696     0.06   0.950    -113.7277      121.26
        hispanic |  -170.7733   54.45239    -3.14   0.002    -277.4981    -64.0486
   nonhisp_black |  -396.6924   695.1677    -0.57   0.568    -1759.196    965.8113
  gte_highschool |  -474.9714   180.4158    -2.63   0.008    -828.5799    -121.363
          age_65 |  -200.6593   256.7303    -0.78   0.434    -703.8415    302.5228
       uninsured |   549.8306   305.3727     1.80   0.072    -48.68884     1148.35
-----------------+----------------------------------------------------------------
ucc_f            |
  cms_wage_index |   .7145821   .6424052     1.11   0.266    -.5445089    1.973673
-----------------+----------------------------------------------------------------
         /ucc_a1 |   664.0656   123.6789     5.37   0.000     421.6595    906.4717
         /ucc_a2 |   171.6256   50.64155     3.39   0.001     72.36999    270.8812
         /ucc_a3 |    20.4698   18.82389     1.09   0.277    -16.42436    57.36395
         /ucc_g1 |   .6417495   .6463895     0.99   0.321    -.6251506     1.90865
         /ucc_g2 |   .1525135   .1525181     1.00   0.317    -.1464164    .4514435
         /ucc_g3 |   .4942792   .1575277     3.14   0.002     .1855307    .8030277
-----------------+----------------------------------------------------------------
aucc_s           |
        tot_pop3 |          1  (constrained)
-----------------+----------------------------------------------------------------
aucc_v           |
     n_hospitals |  -13.36998   12.78761    -1.05   0.296    -38.43323    11.69328
    n_urgentcare |  -49.56391   12.30367    -4.03   0.000    -73.67866   -25.44916
           rural |    101.376   48.33644     2.10   0.036     6.638309    196.1137
        hispanic |  -36.09992   27.64188    -1.31   0.192      -90.277    18.07716
   nonhisp_black |   381.5867   567.3428     0.67   0.501    -730.3848    1493.558
  gte_highschool |  -100.7201    118.251    -0.85   0.394    -332.4878    131.0475
          age_65 |  -229.9781   131.5377    -1.75   0.080    -487.7873      27.831
       uninsured |   90.96487   149.9951     0.61   0.544    -203.0202      384.95
-----------------+----------------------------------------------------------------
aucc_f           |
  cms_wage_index |   1.077071   .5886234     1.83   0.067    -.0766094    2.230752
-----------------+----------------------------------------------------------------
        /aucc_a1 |   286.4573   81.13334     3.53   0.000     127.4389    445.4757
        /aucc_a2 |    .168035   5.551817     0.03   0.976    -10.71333     11.0494
        /aucc_g1 |   .0985935   .5768664     0.17   0.864    -1.032044    1.229231
        /aucc_g2 |   .3501396   .0893536     3.92   0.000     .1750099    .5252694
             /r1 |   .4874472   .1218895     4.00   0.000     .2485482    .7263463
             /r2 |   .4874472   .1218895     4.00   0.000     .2485482    .7263463
             /r3 |   .4874472   .1218895     4.00   0.000     .2485482    .7263463
----------------------------------------------------------------------------------
(est2 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. qui do "BR_EntryThreshold_Bi_3t_Income.do" 10000 10000 10000 "K" 1

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7942631   .6501128     1.22   0.222    -.4799347    2.068461
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.288542   .6543507     1.97   0.049     .0060385    2.571046
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |     492.44   111.6601     4.41   0.000     273.5903    711.2897
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   471.9702   111.8334     4.22   0.000     252.7807    691.1597
------------------------------------------------------------------------------

. nlcom atanh(/r1)

       _nl_1: atanh(/r1)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .5327065   .1598771     3.33   0.001     .2193532    .8460598
------------------------------------------------------------------------------

. 
. * SVI
. 
. ml clear

. ml model lf brentry_bioprobit_neg_3t (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = ru
> ral income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (hosp_f:cat_hosp2 
> = cms_wage_index con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (uc
> c_v:cat_ucc = n_hospitals n_hospaffucc_geo rural income_pc hispanic nonhisp_black gte_highschool
>  age_65 uninsured, nocons) (ucc_f:cat_ucc = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc
> _g1 /ucc_g2 /ucc_g3 (aucc_s:cat_aucc = tot_pop3, nocons) (aucc_v:cat_aucc = n_hospitals n_urgent
> care rural income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (aucc_f:cat
> _aucc = cms_wage_index, nocons) /aucc_a1 /aucc_a2 /aucc_g1 /aucc_g2 /r1 /r2 /r3 if high_svi==1, 
> constraints(1 2 3 4 5) technique(bfgs) 

. eststo: ml max, difficult iterate(300) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -1133.4507
Rescale:      Log likelihood = -964.87947
Rescale eq:   Log likelihood = -684.17773
Iteration 0:  Log likelihood = -1250.7443  
Iteration 1:  Log likelihood = -972.68324  (backed up)
Iteration 2:  Log likelihood = -942.61093  (backed up)
Iteration 3:  Log likelihood =  -940.4916  (backed up)
Iteration 4:  Log likelihood = -939.23737  (backed up)
Iteration 5:  Log likelihood = -936.42977  
Iteration 6:  Log likelihood = -927.78474  (backed up)
Iteration 7:  Log likelihood = -873.06751  
Iteration 8:  Log likelihood = -860.22883  (backed up)
Iteration 9:  Log likelihood = -799.84761  
Iteration 10: Log likelihood = -754.36118  
Iteration 11: Log likelihood = -731.07363  
Iteration 12: Log likelihood = -715.10622  
Iteration 13: Log likelihood = -704.33049  
Iteration 14: Log likelihood = -694.21026  
Iteration 15: Log likelihood = -691.17391  
Iteration 16: Log likelihood = -689.64138  
Iteration 17: Log likelihood = -687.29122  
Iteration 18: Log likelihood = -683.93468  
Iteration 19: Log likelihood = -677.91242  
Iteration 20: Log likelihood = -671.88481  
Iteration 21: Log likelihood = -668.66209  
Iteration 22: Log likelihood = -663.86906  
Iteration 23: Log likelihood = -654.97692  
Iteration 24: Log likelihood = -650.53156  
Iteration 25: Log likelihood = -649.10192  
Iteration 26: Log likelihood = -648.60062  
Iteration 27: Log likelihood = -648.30822  
Iteration 28: Log likelihood = -646.30233  
Iteration 29: Log likelihood = -644.11884  
Iteration 30: Log likelihood = -643.12067  
Iteration 31: Log likelihood = -642.49912  
Iteration 32: Log likelihood = -641.87069  
Iteration 33: Log likelihood = -639.57253  
Iteration 34: Log likelihood = -636.05082  
Iteration 35: Log likelihood = -635.06735  
Iteration 36: Log likelihood = -634.79481  
Iteration 37: Log likelihood = -634.01368  
Iteration 38: Log likelihood = -632.44916  
Iteration 39: Log likelihood = -628.23319  
Iteration 40: Log likelihood = -625.97712  
Iteration 41: Log likelihood = -625.59541  
Iteration 42: Log likelihood = -625.53973  
Iteration 43: Log likelihood = -625.47537  
Iteration 44: Log likelihood =  -624.8811  
Iteration 45: Log likelihood = -621.65041  
Iteration 46: Log likelihood = -619.59593  
Iteration 47: Log likelihood = -619.12146  
Iteration 48: Log likelihood = -618.96485  
Iteration 49: Log likelihood = -618.90801  
Iteration 50: Log likelihood = -618.49268  
Iteration 51: Log likelihood = -616.51166  
Iteration 52: Log likelihood = -615.18454  
Iteration 53: Log likelihood = -615.13089  
Iteration 54: Log likelihood = -615.12239  
Iteration 55: Log likelihood = -615.05683  
Iteration 56: Log likelihood = -614.58345  
Iteration 57: Log likelihood = -613.61545  
Iteration 58: Log likelihood = -613.46071  
Iteration 59: Log likelihood = -613.44625  
Iteration 60: Log likelihood = -613.32259  
Iteration 61: Log likelihood = -610.98057  
Iteration 62: Log likelihood =  -609.8064  
Iteration 63: Log likelihood =  -609.7007  
Iteration 64: Log likelihood =   -609.629  
Iteration 65: Log likelihood = -609.62368  
Iteration 66: Log likelihood = -609.59457  
Iteration 67: Log likelihood = -609.23377  
Iteration 68: Log likelihood = -607.86353  
Iteration 69: Log likelihood = -607.50654  
Iteration 70: Log likelihood = -607.48235  
Iteration 71: Log likelihood = -607.47884  
Iteration 72: Log likelihood = -607.33946  
Iteration 73: Log likelihood =  -606.7331  
Iteration 74: Log likelihood = -606.64834  
Iteration 75: Log likelihood =  -606.6458  
Iteration 76: Log likelihood =  -606.6246  
Iteration 77: Log likelihood = -606.28857  
Iteration 78: Log likelihood = -605.84291  
Iteration 79: Log likelihood = -605.81712  
Iteration 80: Log likelihood = -605.81591  
Iteration 81: Log likelihood = -605.80109  
Iteration 82: Log likelihood = -605.61877  
Iteration 83: Log likelihood = -605.36511  
Iteration 84: Log likelihood = -605.32124  
Iteration 85: Log likelihood = -605.31734  
Iteration 86: Log likelihood = -605.31256  
Iteration 87: Log likelihood = -605.26464  
Iteration 88: Log likelihood = -604.97405  
Iteration 89: Log likelihood = -604.71931  
Iteration 90: Log likelihood = -604.70697  
Iteration 91: Log likelihood =  -604.6886  
Iteration 92: Log likelihood = -604.68814  
Iteration 93: Log likelihood = -604.68455  
Iteration 94: Log likelihood = -604.30436  
Iteration 95: Log likelihood = -603.99044  
Iteration 96: Log likelihood = -603.95316  
Iteration 97: Log likelihood = -603.95213  
Iteration 98: Log likelihood = -603.94813  
Iteration 99: Log likelihood =  -603.4765  
Iteration 100: Log likelihood = -602.86974  
Iteration 101: Log likelihood = -602.84724  
Iteration 102: Log likelihood = -602.84622  
Iteration 103: Log likelihood =  -602.8459  
Iteration 104: Log likelihood = -602.83735  
Iteration 105: Log likelihood = -602.77541  
Iteration 106: Log likelihood =  -602.6841  
Iteration 107: Log likelihood = -602.66954  
Iteration 108: Log likelihood = -602.66926  
Iteration 109: Log likelihood =  -602.6689  
Iteration 110: Log likelihood = -602.64225  
Iteration 111: Log likelihood = -602.48723  
Iteration 112: Log likelihood = -602.43175  
Iteration 113: Log likelihood = -602.43017  
Iteration 114: Log likelihood =  -602.4301  
Iteration 115: Log likelihood = -602.42439  
Iteration 116: Log likelihood = -602.34068  
Iteration 117: Log likelihood = -602.32714  
Iteration 118: Log likelihood = -602.32704  
Iteration 119: Log likelihood = -602.32699  
Iteration 120: Log likelihood = -602.32643  
Iteration 121: Log likelihood = -602.31529  
Iteration 122: Log likelihood = -602.09732  
Iteration 123: Log likelihood = -601.92936  
Iteration 124: Log likelihood = -601.92272  
Iteration 125: Log likelihood = -601.92267  
Iteration 126: Log likelihood =  -601.9226  
Iteration 127: Log likelihood =  -601.9028  
Iteration 128: Log likelihood = -601.82811  
Iteration 129: Log likelihood = -601.80748  
Iteration 130: Log likelihood = -601.80728  
Iteration 131: Log likelihood = -601.80727  
Iteration 132: Log likelihood = -601.80709  
Iteration 133: Log likelihood = -601.80199  
Iteration 134: Log likelihood = -601.77222  
Iteration 135: Log likelihood = -601.76284  
Iteration 136: Log likelihood = -601.76272  
Iteration 137: Log likelihood = -601.76272  
Iteration 138: Log likelihood = -601.76266  
Iteration 139: Log likelihood = -601.75835  
Iteration 140: Log likelihood = -601.73849  
Iteration 141: Log likelihood = -601.73579  
Iteration 142: Log likelihood = -601.73578  
Iteration 143: Log likelihood = -601.73578  
Iteration 144: Log likelihood = -601.73577  
Iteration 145: Log likelihood =  -601.7355  
Iteration 146: Log likelihood = -601.73295  
Iteration 147: Log likelihood = -601.73201  
Iteration 148: Log likelihood = -601.73201  
Iteration 149: Log likelihood = -601.73201  
Iteration 150: Log likelihood = -601.73201  
Iteration 151: Log likelihood =   -601.732  
Iteration 152: Log likelihood = -601.73178  
Iteration 153: Log likelihood = -601.73176  
Iteration 154: Log likelihood = -601.73176  
Iteration 155: Log likelihood = -601.73176  
Iteration 156: Log likelihood = -601.73176  
Iteration 157: Log likelihood = -601.73176  

                                                           Number of obs = 337
                                                           Wald chi2(0)  =   .
Log likelihood = -601.73176                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
 ( 3)  [aucc_s]tot_pop3 = 1
 ( 4)  [/]r1 - [/]r2 = 0
 ( 5)  [/]r2 - [/]r3 = 0
----------------------------------------------------------------------------------
                 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
hosp_s           |
         tot_pop |          1  (constrained)
-----------------+----------------------------------------------------------------
hosp_v           |
           rural |   127.1056    57.7552     2.20   0.028      13.9075    240.3037
       income_pc |  -5.726005   13.55654    -0.42   0.673    -32.29633    20.84432
        hispanic |  -32.32123   48.94126    -0.66   0.509    -128.2443    63.60189
   nonhisp_black |   423.0683   265.1005     1.60   0.111    -96.51911    942.6556
  gte_highschool |  -63.36558    229.395    -0.28   0.782    -512.9715    386.2403
          age_65 |   317.9695   201.8149     1.58   0.115     -77.5805    713.5195
       uninsured |  -76.90668   195.2416    -0.39   0.694    -459.5731    305.7598
-----------------+----------------------------------------------------------------
hosp_f           |
  cms_wage_index |    1.16073   .7306243     1.59   0.112    -.2712675    2.592727
   con_intensity |   .5830212    .330864     1.76   0.078    -.0654602    1.231503
-----------------+----------------------------------------------------------------
        /hosp_a1 |   106.7363    98.8551     1.08   0.280    -87.01615    300.4887
        /hosp_g1 |   .3274785   .7140281     0.46   0.646    -1.071991    1.726948
-----------------+----------------------------------------------------------------
ucc_s            |
        tot_pop2 |          1  (constrained)
-----------------+----------------------------------------------------------------
ucc_v            |
     n_hospitals |  -91.48578   23.64951    -3.87   0.000     -137.838    -45.1336
n_hospaffucc_geo |  -58.72784   13.28985    -4.42   0.000    -84.77547   -32.68021
           rural |   128.7446   57.37932     2.24   0.025     16.28323     241.206
       income_pc |   31.36764   34.50711     0.91   0.363    -36.26505    99.00032
        hispanic |   14.61708   75.96494     0.19   0.847    -134.2715    163.5056
   nonhisp_black |  -196.1332   263.9491    -0.74   0.457    -713.4639    321.1976
  gte_highschool |   920.5939   457.2538     2.01   0.044     24.39294    1816.795
          age_65 |   362.9926   272.3163     1.33   0.183    -170.7375    896.7227
       uninsured |   255.9158   270.5119     0.95   0.344    -274.2778    786.1094
-----------------+----------------------------------------------------------------
ucc_f            |
  cms_wage_index |  -.2542818   .5568806    -0.46   0.648    -1.345748    .8371842
-----------------+----------------------------------------------------------------
         /ucc_a1 |  -12.18523   217.9891    -0.06   0.955    -439.4361    415.0656
         /ucc_a2 |   226.6908   57.56679     3.94   0.000     113.8619    339.5196
         /ucc_a3 |  -40.89397   23.30034    -1.76   0.079     -86.5618     4.77386
         /ucc_g1 |   1.959452   .5816404     3.37   0.001     .8194579    3.099446
         /ucc_g2 |   .1299559   .1790185     0.73   0.468    -.2209138    .4808256
         /ucc_g3 |   .8557231   .2126766     4.02   0.000     .4388847    1.272562
-----------------+----------------------------------------------------------------
aucc_s           |
        tot_pop3 |          1  (constrained)
-----------------+----------------------------------------------------------------
aucc_v           |
     n_hospitals |  -41.63307   14.76555    -2.82   0.005    -70.57302   -12.69313
    n_urgentcare |  -44.84394   11.85925    -3.78   0.000    -68.08764   -21.60025
           rural |   137.6443   48.44827     2.84   0.004     42.68746    232.6012
       income_pc |  -4.362406   11.07451    -0.39   0.694    -26.06804    17.34323
        hispanic |  -75.40748   25.03225    -3.01   0.003    -124.4698   -26.34517
   nonhisp_black |  -459.3098   273.7662    -1.68   0.093    -995.8817    77.26218
  gte_highschool |     122.22    129.377     0.94   0.345    -131.3543    375.7943
          age_65 |  -90.47304   129.2579    -0.70   0.484    -343.8139    162.8678
       uninsured |   218.8485   131.3582     1.67   0.096    -38.60885    476.3058
-----------------+----------------------------------------------------------------
aucc_f           |
  cms_wage_index |  -.7306688    .537945    -1.36   0.174    -1.785022     .323684
-----------------+----------------------------------------------------------------
        /aucc_a1 |   199.5823   67.75908     2.95   0.003     66.77691    332.3876
        /aucc_a2 |   .2218922   5.689638     0.04   0.969    -10.92959    11.37338
        /aucc_g1 |   2.088627   .5450488     3.83   0.000     1.020351    3.156903
        /aucc_g2 |   .5270953   .0988843     5.33   0.000     .3332856     .720905
             /r1 |   .5922291   .1303823     4.54   0.000     .3366844    .8477738
             /r2 |   .5922291   .1303823     4.54   0.000     .3366844    .8477738
             /r3 |   .5922291   .1303823     4.54   0.000     .3366844    .8477738
----------------------------------------------------------------------------------
(est3 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. qui do "BR_EntryThreshold_Bi_3t_SVI.do" 10000 10000 10000 "L" 1

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   2.089408   .5741249     3.64   0.000      .964144    3.214672
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   2.945131   .6040266     4.88   0.000     1.761261    4.129001
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   -238.876   217.4015    -1.10   0.272    -664.9751    187.2231
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   -197.982   220.7855    -0.90   0.370    -630.7136    234.7496
------------------------------------------------------------------------------

. nlcom atanh(/r1)

       _nl_1: atanh(/r1)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .6810923   .2008154     3.39   0.001     .2875014    1.074683
------------------------------------------------------------------------------

. 
. ml clear

. ml model lf brentry_bioprobit_neg_3t (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = ru
> ral income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (hosp_f:cat_hosp2 
> = cms_wage_index con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (uc
> c_v:cat_ucc = n_hospitals n_hospaffucc_geo rural income_pc hispanic nonhisp_black gte_highschool
>  age_65 uninsured, nocons) (ucc_f:cat_ucc = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc
> _g1 /ucc_g2 /ucc_g3 (aucc_s:cat_aucc = tot_pop3, nocons) (aucc_v:cat_aucc = n_hospitals n_urgent
> care rural income_pc hispanic nonhisp_black gte_highschool age_65 uninsured, nocons) (aucc_f:cat
> _aucc = cms_wage_index, nocons) /aucc_a1 /aucc_a2 /aucc_g1 /aucc_g2 /r1 /r2 /r3 if high_svi==0, 
> constraints(1 2 3 4 5) technique(bfgs) 

. eststo: ml max, difficult iterate(300) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -1035.3524
Rescale:      Log likelihood = -900.49634
Rescale eq:   Log likelihood =  -693.5263
Iteration 0:  Log likelihood = -1027.0388  
Iteration 1:  Log likelihood = -971.51943  (backed up)
Iteration 2:  Log likelihood =  -965.1242  (backed up)
Iteration 3:  Log likelihood = -962.67725  (backed up)
Iteration 4:  Log likelihood = -960.87197  (backed up)
Iteration 5:  Log likelihood = -958.99097  (backed up)
Iteration 6:  Log likelihood = -957.25234  (backed up)
Iteration 7:  Log likelihood = -956.46414  (backed up)
Iteration 8:  Log likelihood = -912.43999  (backed up)
Iteration 9:  Log likelihood = -813.71813  
Iteration 10: Log likelihood =  -813.7148  (backed up)
Iteration 11: Log likelihood = -813.71456  (backed up)
Iteration 12: Log likelihood = -813.71672  (backed up)
Iteration 13: Log likelihood = -812.73894  (backed up)
Iteration 14: Log likelihood = -811.27232  (backed up)
Iteration 15: Log likelihood = -791.63505  
Iteration 16: Log likelihood = -764.83531  
Iteration 17: Log likelihood = -737.63483  
Iteration 18: Log likelihood = -734.35724  
Iteration 19: Log likelihood =  -732.1265  
Iteration 20: Log likelihood = -728.35029  
Iteration 21: Log likelihood = -719.25007  
Iteration 22: Log likelihood = -713.41726  
Iteration 23: Log likelihood = -710.05173  
Iteration 24: Log likelihood =  -707.2604  
Iteration 25: Log likelihood = -705.09101  
Iteration 26: Log likelihood = -704.39404  
Iteration 27: Log likelihood =   -703.914  
Iteration 28: Log likelihood = -703.08067  
Iteration 29: Log likelihood = -701.67848  
Iteration 30: Log likelihood = -698.48511  
Iteration 31: Log likelihood = -695.15236  
Iteration 32: Log likelihood = -692.53393  
Iteration 33: Log likelihood = -690.76403  
Iteration 34: Log likelihood = -685.80375  
Iteration 35: Log likelihood = -678.02478  
Iteration 36: Log likelihood = -674.06787  
Iteration 37: Log likelihood = -673.65942  
Iteration 38: Log likelihood = -672.98411  
Iteration 39: Log likelihood = -669.63074  
Iteration 40: Log likelihood = -667.48371  
Iteration 41: Log likelihood = -667.06471  
Iteration 42: Log likelihood = -666.80052  
Iteration 43: Log likelihood = -665.67897  
Iteration 44: Log likelihood = -663.67255  
Iteration 45: Log likelihood = -663.02186  
Iteration 46: Log likelihood = -662.76721  
Iteration 47: Log likelihood = -661.15095  
Iteration 48: Log likelihood = -654.90457  
Iteration 49: Log likelihood = -653.15079  
Iteration 50: Log likelihood = -652.40829  
Iteration 51: Log likelihood = -651.73421  
Iteration 52: Log likelihood =  -651.5192  
Iteration 53: Log likelihood = -650.80636  
Iteration 54: Log likelihood = -648.45571  
Iteration 55: Log likelihood =  -647.3186  
Iteration 56: Log likelihood = -647.25868  
Iteration 57: Log likelihood =  -647.2463  
Iteration 58: Log likelihood = -647.09484  
Iteration 59: Log likelihood = -645.71148  
Iteration 60: Log likelihood = -645.45247  
Iteration 61: Log likelihood = -645.44214  
Iteration 62: Log likelihood =  -645.3951  
Iteration 63: Log likelihood = -643.69852  
Iteration 64: Log likelihood =  -643.0371  
Iteration 65: Log likelihood = -643.01511  
Iteration 66: Log likelihood = -643.00127  
Iteration 67: Log likelihood = -642.64083  
Iteration 68: Log likelihood = -642.53971  
Iteration 69: Log likelihood = -642.53382  
Iteration 70: Log likelihood = -642.51311  
Iteration 71: Log likelihood = -642.24688  
Iteration 72: Log likelihood = -641.85237  
Iteration 73: Log likelihood = -641.80379  
Iteration 74: Log likelihood = -641.80118  
Iteration 75: Log likelihood = -641.73801  
Iteration 76: Log likelihood = -640.97334  
Iteration 77: Log likelihood = -638.81638  
Iteration 78: Log likelihood = -638.23378  
Iteration 79: Log likelihood = -638.22059  
Iteration 80: Log likelihood = -638.22014  
Iteration 81: Log likelihood = -638.19076  
Iteration 82: Log likelihood = -638.04424  
Iteration 83: Log likelihood = -637.99358  
Iteration 84: Log likelihood = -637.99259  
Iteration 85: Log likelihood = -637.98998  
Iteration 86: Log likelihood = -637.86051  
Iteration 87: Log likelihood = -637.29779  
Iteration 88: Log likelihood = -637.19584  
Iteration 89: Log likelihood = -637.19467  
Iteration 90: Log likelihood = -637.17935  
Iteration 91: Log likelihood = -636.87668  
Iteration 92: Log likelihood = -636.16751  
Iteration 93: Log likelihood = -636.09502  
Iteration 94: Log likelihood =  -636.0945  
Iteration 95: Log likelihood = -636.08841  
Iteration 96: Log likelihood = -636.01436  
Iteration 97: Log likelihood = -635.59832  
Iteration 98: Log likelihood = -635.37124  
Iteration 99: Log likelihood = -635.36375  
Iteration 100: Log likelihood = -635.36266  
Iteration 101: Log likelihood = -635.32213  
Iteration 102: Log likelihood = -635.12753  
Iteration 103: Log likelihood =  -635.0444  
Iteration 104: Log likelihood = -635.04301  
Iteration 105: Log likelihood = -635.04292  
Iteration 106: Log likelihood = -635.04211  
Iteration 107: Log likelihood = -635.02176  
Iteration 108: Log likelihood = -634.91998  
Iteration 109: Log likelihood = -634.89288  
Iteration 110: Log likelihood = -634.89259  
Iteration 111: Log likelihood = -634.89251  
Iteration 112: Log likelihood = -634.88797  
Iteration 113: Log likelihood = -634.64035  
Iteration 114: Log likelihood = -634.49118  
Iteration 115: Log likelihood =  -634.4878  
Iteration 116: Log likelihood = -634.48778  
Iteration 117: Log likelihood = -634.48769  
Iteration 118: Log likelihood = -634.48486  
Iteration 119: Log likelihood = -634.30864  
Iteration 120: Log likelihood = -634.23997  
Iteration 121: Log likelihood = -634.23969  
Iteration 122: Log likelihood = -634.23935  
Iteration 123: Log likelihood =  -634.1954  
Iteration 124: Log likelihood = -634.11855  
Iteration 125: Log likelihood = -634.11613  
Iteration 126: Log likelihood = -634.11612  
Iteration 127: Log likelihood =  -634.1159  
Iteration 128: Log likelihood = -634.11201  
Iteration 129: Log likelihood = -633.96583  
Iteration 130: Log likelihood = -633.95359  
Iteration 131: Log likelihood = -633.95358  
Iteration 132: Log likelihood = -633.95355  
Iteration 133: Log likelihood = -633.93257  
Iteration 134: Log likelihood = -633.80876  
Iteration 135: Log likelihood = -633.69727  
Iteration 136: Log likelihood = -633.69445  
Iteration 137: Log likelihood = -633.69444  
Iteration 138: Log likelihood =  -633.6944  
Iteration 139: Log likelihood = -633.69288  
Iteration 140: Log likelihood = -633.61454  
Iteration 141: Log likelihood = -633.28529  
Iteration 142: Log likelihood =  -633.2257  
Iteration 143: Log likelihood = -633.22526  
Iteration 144: Log likelihood = -633.22518  
Iteration 145: Log likelihood = -633.22106  
Iteration 146: Log likelihood = -633.07241  
Iteration 147: Log likelihood = -630.89501  
Iteration 148: Log likelihood = -630.73691  
Iteration 149: Log likelihood = -630.73471  
Iteration 150: Log likelihood = -630.73467  
Iteration 151: Log likelihood = -630.73451  
Iteration 152: Log likelihood = -630.72616  
Iteration 153: Log likelihood = -630.54038  
Iteration 154: Log likelihood = -629.72106  
Iteration 155: Log likelihood =  -629.3685  
Iteration 156: Log likelihood = -629.36435  
Iteration 157: Log likelihood = -629.36428  
Iteration 158: Log likelihood = -629.36428  
Iteration 159: Log likelihood = -629.36427  
Iteration 160: Log likelihood = -629.36426  
Iteration 161: Log likelihood = -629.36367  
Iteration 162: Log likelihood = -629.35676  
Iteration 163: Log likelihood = -629.34743  
Iteration 164: Log likelihood = -629.34652  
Iteration 165: Log likelihood = -629.34651  
Iteration 166: Log likelihood = -629.34651  
Iteration 167: Log likelihood = -629.34651  
Iteration 168: Log likelihood = -629.34651  
Iteration 169: Log likelihood = -629.34645  
Iteration 170: Log likelihood = -629.34566  
Iteration 171: Log likelihood = -629.33893  
Iteration 172: Log likelihood = -629.33636  
Iteration 173: Log likelihood = -629.33635  
Iteration 174: Log likelihood = -629.33634  
Iteration 175: Log likelihood = -629.33634  
Iteration 176: Log likelihood = -629.33634  
Iteration 177: Log likelihood = -629.33634  
Iteration 178: Log likelihood = -629.33631  
Iteration 179: Log likelihood = -629.33587  
Iteration 180: Log likelihood = -629.33515  
Iteration 181: Log likelihood = -629.33503  
Iteration 182: Log likelihood = -629.33503  
Iteration 183: Log likelihood = -629.33503  
Iteration 184: Log likelihood = -629.33503  
Iteration 185: Log likelihood = -629.33503  
Iteration 186: Log likelihood = -629.33503  
Iteration 187: Log likelihood =   -629.335  
Iteration 188: Log likelihood = -629.33492  
Iteration 189: Log likelihood = -629.33491  
Iteration 190: Log likelihood = -629.33491  
Iteration 191: Log likelihood = -629.33491  
Iteration 192: Log likelihood = -629.33491  
Iteration 193: Log likelihood = -629.33491  
Iteration 194: Log likelihood = -629.33491  
Iteration 195: Log likelihood = -629.33491  
Iteration 196: Log likelihood = -629.33491  
Iteration 197: Log likelihood = -629.33491  
Iteration 198: Log likelihood = -629.33491  
Iteration 199: Log likelihood = -629.33491  
Iteration 200: Log likelihood = -629.33403  
Iteration 201: Log likelihood = -629.18415  
Iteration 202: Log likelihood = -627.46926  
Iteration 203: Log likelihood = -626.56071  
Iteration 204: Log likelihood = -626.52509  
Iteration 205: Log likelihood = -626.52465  
Iteration 206: Log likelihood = -626.52463  
Iteration 207: Log likelihood = -626.52463  
Iteration 208: Log likelihood = -626.52463  
Iteration 209: Log likelihood = -626.52463  
Iteration 210: Log likelihood = -626.52463  (backed up)
Iteration 211: Log likelihood = -626.52463  (backed up)
Iteration 212: Log likelihood = -626.52463  
Iteration 213: Log likelihood = -626.52463  
Iteration 214: Log likelihood = -626.52463  
Iteration 215: Log likelihood = -626.52461  
Iteration 216: Log likelihood = -626.52415  
Iteration 217: Log likelihood = -626.49866  
Iteration 218: Log likelihood = -626.22404  
Iteration 219: Log likelihood = -626.12309  
Iteration 220: Log likelihood = -626.12127  
Iteration 221: Log likelihood = -626.12127  
Iteration 222: Log likelihood = -626.12127  (backed up)
Iteration 223: Log likelihood = -626.12103  
Iteration 224: Log likelihood = -626.11851  
Iteration 225: Log likelihood = -625.99427  
Iteration 226: Log likelihood = -625.73915  
Iteration 227: Log likelihood = -625.72563  
Iteration 228: Log likelihood = -625.72558  
Iteration 229: Log likelihood = -625.72548  
Iteration 230: Log likelihood = -625.72198  
Iteration 231: Log likelihood = -625.65158  
Iteration 232: Log likelihood = -625.64166  
Iteration 233: Log likelihood = -625.64162  
Iteration 234: Log likelihood = -625.64162  
Iteration 235: Log likelihood = -625.64162  
Iteration 236: Log likelihood = -625.64162  (backed up)
Iteration 237: Log likelihood = -625.64162  
Iteration 238: Log likelihood = -625.64162  
Iteration 239: Log likelihood = -625.64162  
Iteration 240: Log likelihood = -625.64162  
Iteration 241: Log likelihood = -625.64144  
Iteration 242: Log likelihood = -625.63361  
Iteration 243: Log likelihood = -625.53497  
Iteration 244: Log likelihood =  -624.2284  
Iteration 245: Log likelihood = -622.37317  
Iteration 246: Log likelihood = -622.15827  
Iteration 247: Log likelihood = -622.15625  
Iteration 248: Log likelihood = -622.15622  

                                                           Number of obs = 336
                                                           Wald chi2(0)  =   .
Log likelihood = -622.15622                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
 ( 3)  [aucc_s]tot_pop3 = 1
 ( 4)  [/]r1 - [/]r2 = 0
 ( 5)  [/]r2 - [/]r3 = 0
----------------------------------------------------------------------------------
                 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
hosp_s           |
         tot_pop |          1  (constrained)
-----------------+----------------------------------------------------------------
hosp_v           |
           rural |   95.98363   72.61191     1.32   0.186    -46.33309    238.3004
       income_pc |   21.08677    17.5567     1.20   0.230    -13.32374    55.49727
        hispanic |  -320.6763   97.99735    -3.27   0.001    -512.7476    -128.605
   nonhisp_black |   1070.899   717.4561     1.49   0.136    -335.2893    2477.087
  gte_highschool |  -529.5291   346.1803    -1.53   0.126     -1208.03    148.9718
          age_65 |   282.6515   292.4338     0.97   0.334    -290.5083    855.8112
       uninsured |   238.2184   275.2855     0.87   0.387    -301.3312    777.7681
-----------------+----------------------------------------------------------------
hosp_f           |
  cms_wage_index |   .3828418   .8711537     0.44   0.660    -1.324588    2.090272
   con_intensity |   .7925897   .3469545     2.28   0.022     .1125714    1.472608
-----------------+----------------------------------------------------------------
        /hosp_a1 |   251.5504   143.3478     1.75   0.079    -29.40608    532.5069
        /hosp_g1 |   .8860547   .8405682     1.05   0.292    -.7614287    2.533538
-----------------+----------------------------------------------------------------
ucc_s            |
        tot_pop2 |          1  (constrained)
-----------------+----------------------------------------------------------------
ucc_v            |
     n_hospitals |  -40.41889   22.58809    -1.79   0.074    -84.69074     3.85296
n_hospaffucc_geo |  -42.15377   10.59811    -3.98   0.000    -62.92568   -21.38186
           rural |   28.86773   69.06374     0.42   0.676    -106.4947    164.2302
       income_pc |  -12.05211    17.1145    -0.70   0.481    -45.59591     21.4917
        hispanic |  -171.6192   126.5742    -1.36   0.175       -419.7    76.46165
   nonhisp_black |  -474.2744   649.2925    -0.73   0.465    -1746.864    798.3155
  gte_highschool |   -113.753   348.2851    -0.33   0.744    -796.3792    568.8732
          age_65 |  -180.5431   330.4069    -0.55   0.585    -828.1287    467.0425
       uninsured |   262.8915    370.036     0.71   0.477    -462.3657    988.1487
-----------------+----------------------------------------------------------------
ucc_f            |
  cms_wage_index |   1.119852   .8210316     1.36   0.173    -.4893404    2.729044
-----------------+----------------------------------------------------------------
         /ucc_a1 |   599.0269   159.0024     3.77   0.000      287.388    910.6658
         /ucc_a2 |   203.5713   57.09144     3.57   0.000      91.6741    315.4684
         /ucc_a3 |   34.58411   19.32698     1.79   0.074    -3.296065    72.46429
         /ucc_g1 |   .4438312   .7921768     0.56   0.575    -1.108807    1.996469
         /ucc_g2 |   .2091157   .1703255     1.23   0.220    -.1247161    .5429475
         /ucc_g3 |   .3537036   .1403862     2.52   0.012     .0785517    .6288555
-----------------+----------------------------------------------------------------
aucc_s           |
        tot_pop3 |          1  (constrained)
-----------------+----------------------------------------------------------------
aucc_v           |
     n_hospitals |  -21.25106   15.32862    -1.39   0.166     -51.2946    8.792473
    n_urgentcare |  -57.71353   12.74614    -4.53   0.000    -82.69551   -32.73156
           rural |   77.10972   57.14045     1.35   0.177     -34.8835    189.1029
       income_pc |   -6.39013   13.62735    -0.47   0.639    -33.09925    20.31899
        hispanic |  -110.1549   76.04129    -1.45   0.147    -259.1931    38.88325
   nonhisp_black |  -301.6412   628.1351    -0.48   0.631    -1532.763    929.4809
  gte_highschool |   8.816036   276.4093     0.03   0.975    -532.9363    550.5683
          age_65 |   118.0434   231.1233     0.51   0.610    -334.9499    571.0368
       uninsured |   244.0391    218.164     1.12   0.263    -183.5544    671.6326
-----------------+----------------------------------------------------------------
aucc_f           |
  cms_wage_index |   .5755092   .7855983     0.73   0.464    -.9642351    2.115254
-----------------+----------------------------------------------------------------
        /aucc_a1 |   246.1743   122.8843     2.00   0.045     5.325548     487.023
        /aucc_a2 |  -1.707418   6.733413    -0.25   0.800    -14.90466    11.48983
        /aucc_g1 |   .8292937   .7514686     1.10   0.270    -.6435577    2.302145
        /aucc_g2 |   .4250403   .0960299     4.43   0.000     .2368252    .6132554
             /r1 |   .4146401   .1092551     3.80   0.000      .200504    .6287761
             /r2 |   .4146401   .1092551     3.80   0.000      .200504    .6287761
             /r3 |   .4146401   .1092551     3.80   0.000      .200504    .6287761
----------------------------------------------------------------------------------
(est4 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. qui do "BR_EntryThreshold_Bi_3t_SVI.do" 10000 10000 10000 "M" 0

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6529469   .7882317     0.83   0.407    -.8919589    2.197853
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.006651   .7919747     1.27   0.204    -.5455913    2.558892
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   395.4556   151.6307     2.61   0.009     98.26498    692.6463
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   360.8715   151.4312     2.38   0.017     64.07189    657.6711
------------------------------------------------------------------------------

. nlcom atanh(/r1)

       _nl_1: atanh(/r1)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .4412018   .1319389     3.34   0.001     .1826063    .6997972
------------------------------------------------------------------------------

. 
. * Uninsured
. 
. ml clear

. ml model lf brentry_bioprobit_neg_3t (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = ru
> ral income_pc hispanic income_pc nonhisp_black gte_highschool age_65, nocons) (hosp_f:cat_hosp2 
> = cms_wage_index con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (uc
> c_v:cat_ucc = n_hospitals n_hospaffucc_geo rural hispanic income_pc nonhisp_black gte_highschool
>  age_65, nocons) (ucc_f:cat_ucc = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g
> 2 /ucc_g3 (aucc_s:cat_aucc = tot_pop3, nocons) (aucc_v:cat_aucc = n_hospitals n_urgentcare rural
>  hispanic income_pc nonhisp_black gte_highschool age_65, nocons) (aucc_f:cat_aucc = cms_wage_ind
> ex, nocons) /aucc_a1 /aucc_a2 /aucc_g1 /aucc_g2 /r1 /r2 /r3 if high_uninsured==1, constraints(1 
> 2 3 4 5) technique(bfgs)
note: income_pc omitted because of collinearity.

. eststo: ml max, difficult iterate(100) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -1008.0901
Rescale:      Log likelihood = -886.20132
Rescale eq:   Log likelihood = -633.79443
Iteration 0:  Log likelihood =  -1118.286  
Iteration 1:  Log likelihood = -891.64156  (backed up)
Iteration 2:  Log likelihood = -873.39077  (backed up)
Iteration 3:  Log likelihood = -852.25923  (backed up)
Iteration 4:  Log likelihood = -848.88974  (backed up)
Iteration 5:  Log likelihood = -848.52079  (backed up)
Iteration 6:  Log likelihood = -847.43843  (backed up)
Iteration 7:  Log likelihood =  -846.7915  (backed up)
Iteration 8:  Log likelihood = -829.64055  
Iteration 9:  Log likelihood = -829.39531  
Iteration 10: Log likelihood = -712.80172  
Iteration 11: Log likelihood = -690.98658  
Iteration 12: Log likelihood = -668.38161  
Iteration 13: Log likelihood = -657.91711  
Iteration 14: Log likelihood = -652.82268  
Iteration 15: Log likelihood = -650.12425  
Iteration 16: Log likelihood = -648.09462  
Iteration 17: Log likelihood = -645.46032  
Iteration 18: Log likelihood = -643.87448  
Iteration 19: Log likelihood = -642.09535  
Iteration 20: Log likelihood = -641.28751  
Iteration 21: Log likelihood = -639.15934  
Iteration 22: Log likelihood = -635.50815  
Iteration 23: Log likelihood = -630.36005  
Iteration 24: Log likelihood = -622.94412  
Iteration 25: Log likelihood = -621.13302  
Iteration 26: Log likelihood = -619.39697  
Iteration 27: Log likelihood = -618.64202  
Iteration 28: Log likelihood = -618.43951  
Iteration 29: Log likelihood = -618.21196  
Iteration 30: Log likelihood = -617.80804  
Iteration 31: Log likelihood = -616.55897  
Iteration 32: Log likelihood = -614.80179  
Iteration 33: Log likelihood = -613.84393  
Iteration 34: Log likelihood = -612.83251  
Iteration 35: Log likelihood =  -612.3924  
Iteration 36: Log likelihood = -610.30848  
Iteration 37: Log likelihood = -604.74322  
Iteration 38: Log likelihood = -602.97058  
Iteration 39: Log likelihood =  -602.2832  
Iteration 40: Log likelihood = -601.23556  
Iteration 41: Log likelihood = -600.30685  
Iteration 42: Log likelihood =  -599.7539  
Iteration 43: Log likelihood = -599.21053  
Iteration 44: Log likelihood = -596.53163  
Iteration 45: Log likelihood = -594.64938  
Iteration 46: Log likelihood = -594.30543  
Iteration 47: Log likelihood = -594.27398  
Iteration 48: Log likelihood = -594.19271  
Iteration 49: Log likelihood = -593.40725  
Iteration 50: Log likelihood = -591.06337  
Iteration 51: Log likelihood = -589.16442  
Iteration 52: Log likelihood = -588.93618  
Iteration 53: Log likelihood = -588.90484  
Iteration 54: Log likelihood = -588.72786  
Iteration 55: Log likelihood = -587.77031  
Iteration 56: Log likelihood = -586.55264  
Iteration 57: Log likelihood = -586.38535  
Iteration 58: Log likelihood = -586.37831  
Iteration 59: Log likelihood = -586.32083  
Iteration 60: Log likelihood = -585.08686  
Iteration 61: Log likelihood = -583.65861  
Iteration 62: Log likelihood = -583.46494  
Iteration 63: Log likelihood = -583.45069  
Iteration 64: Log likelihood = -583.18242  
Iteration 65: Log likelihood = -581.14735  
Iteration 66: Log likelihood = -579.99269  
Iteration 67: Log likelihood = -579.94891  
Iteration 68: Log likelihood = -579.94333  
Iteration 69: Log likelihood = -579.87465  
Iteration 70: Log likelihood = -578.88017  
Iteration 71: Log likelihood = -578.50273  
Iteration 72: Log likelihood = -578.49172  
Iteration 73: Log likelihood = -578.47874  
Iteration 74: Log likelihood = -578.02566  
Iteration 75: Log likelihood = -577.10237  
Iteration 76: Log likelihood = -576.94616  
Iteration 77: Log likelihood = -576.93934  
Iteration 78: Log likelihood = -576.93384  
Iteration 79: Log likelihood =  -576.8004  
Iteration 80: Log likelihood = -576.21726  
Iteration 81: Log likelihood = -576.13869  
Iteration 82: Log likelihood = -576.13618  
Iteration 83: Log likelihood = -576.12399  
Iteration 84: Log likelihood = -575.89076  
Iteration 85: Log likelihood = -575.56506  
Iteration 86: Log likelihood = -575.52693  
Iteration 87: Log likelihood = -575.52556  
Iteration 88: Log likelihood = -575.51214  
Iteration 89: Log likelihood = -575.06314  
Iteration 90: Log likelihood =  -574.0724  
Iteration 91: Log likelihood = -574.03623  
Iteration 92: Log likelihood = -574.03547  
Iteration 93: Log likelihood = -574.03035  
Iteration 94: Log likelihood = -573.96813  
Iteration 95: Log likelihood = -573.27845  
Iteration 96: Log likelihood = -572.47188  
Iteration 97: Log likelihood = -572.38891  
Iteration 98: Log likelihood = -572.38759  
Iteration 99: Log likelihood =  -572.3846  
Iteration 100: Log likelihood = -572.26715  
convergence not achieved

                                                           Number of obs = 337
                                                           Wald chi2(0)  =   .
Log likelihood = -572.26715                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
 ( 3)  [aucc_s]tot_pop3 = 1
 ( 4)  [/]r1 - [/]r2 = 0
 ( 5)  [/]r2 - [/]r3 = 0
----------------------------------------------------------------------------------
                 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
hosp_s           |
         tot_pop |          1  (constrained)
-----------------+----------------------------------------------------------------
hosp_v           |
           rural |   100.2986   68.49633     1.46   0.143    -33.95174    234.5489
       income_pc |  -15.64368   15.97806    -0.98   0.328     -46.9601    15.67274
        hispanic |   -58.4477   47.47624    -1.23   0.218    -151.4994    34.60402
       income_pc |          0  (omitted)
   nonhisp_black |   498.1971   256.1168     1.95   0.052    -3.782697    1000.177
  gte_highschool |  -44.08824   235.6702    -0.19   0.852    -505.9933    417.8168
          age_65 |   157.6063   192.9315     0.82   0.414    -220.5325    535.7452
-----------------+----------------------------------------------------------------
hosp_f           |
  cms_wage_index |   .2139286    .789545     0.27   0.786    -1.333551    1.761408
   con_intensity |   .5292167   .3775817     1.40   0.161    -.2108299    1.269263
-----------------+----------------------------------------------------------------
        /hosp_a1 |    142.361    106.362     1.34   0.181    -66.10464    350.8266
        /hosp_g1 |   1.152168   .7665413     1.50   0.133    -.3502252    2.654561
-----------------+----------------------------------------------------------------
ucc_s            |
        tot_pop2 |          1  (constrained)
-----------------+----------------------------------------------------------------
ucc_v            |
     n_hospitals |  -67.84331   26.26348    -2.58   0.010    -119.3188   -16.36784
n_hospaffucc_geo |  -46.18013   14.89684    -3.10   0.002     -75.3774   -16.98286
           rural |   6.946606   69.03217     0.10   0.920     -128.354    142.2472
        hispanic |  -76.45154   70.30429    -1.09   0.277    -214.2454    61.34234
       income_pc |   -20.2359   37.07625    -0.55   0.585    -92.90401    52.43221
   nonhisp_black |  -196.7937   264.1997    -0.74   0.456    -714.6157    321.0282
  gte_highschool |   528.9672   454.9612     1.16   0.245    -362.7404    1420.675
          age_65 |   225.7545   266.5533     0.85   0.397    -296.6804    748.1894
-----------------+----------------------------------------------------------------
ucc_f            |
  cms_wage_index |  -.0226231   .7282581    -0.03   0.975    -1.449983    1.404737
-----------------+----------------------------------------------------------------
         /ucc_a1 |   409.0043   176.6683     2.32   0.021     62.74073    755.2679
         /ucc_a2 |    293.727   62.58273     4.69   0.000     171.0672    416.3869
         /ucc_a3 |  -14.75974    20.5929    -0.72   0.474    -55.12107     25.6016
         /ucc_g1 |   1.711016   .7190156     2.38   0.017     .3017717    3.120261
         /ucc_g2 |   .0803726   .1660527     0.48   0.628    -.2450847    .4058298
         /ucc_g3 |    .575823   .1778599     3.24   0.001      .227224    .9244221
-----------------+----------------------------------------------------------------
aucc_s           |
        tot_pop3 |          1  (constrained)
-----------------+----------------------------------------------------------------
aucc_v           |
     n_hospitals |  -31.22662   15.63346    -2.00   0.046    -61.86764    -.585606
    n_urgentcare |  -51.74744   12.99989    -3.98   0.000    -77.22676   -26.26812
           rural |   90.51522   59.49613     1.52   0.128    -26.09504    207.1255
        hispanic |  -83.86409   22.97443    -3.65   0.000    -128.8931   -38.83504
       income_pc |  -18.76012   12.03587    -1.56   0.119       -42.35    4.829751
   nonhisp_black |  -517.4439   282.9806    -1.83   0.067    -1072.076      37.188
  gte_highschool |   119.1952   133.7426     0.89   0.373    -142.9355    381.3259
          age_65 |  -159.1016   130.6991    -1.22   0.223    -415.2671    97.06399
-----------------+----------------------------------------------------------------
aucc_f           |
  cms_wage_index |  -.4113266   .6676227    -0.62   0.538    -1.719843    .8971899
-----------------+----------------------------------------------------------------
        /aucc_a1 |   297.2517   63.38538     4.69   0.000     173.0186    421.4847
        /aucc_a2 |   .8334514   5.698583     0.15   0.884    -10.33557    12.00247
        /aucc_g1 |   1.783547   .6494474     2.75   0.006     .5106535    3.056441
        /aucc_g2 |   .4562397   .0946831     4.82   0.000     .2706643    .6418151
             /r1 |   .4681733   .1169357     4.00   0.000     .2389835    .6973631
             /r2 |   .4681733   .1169357     4.00   0.000     .2389835    .6973631
             /r3 |   .4681733   .1169357     4.00   0.000     .2389835    .6973631
----------------------------------------------------------------------------------
Warning: Convergence not achieved.
(est5 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. qui do "BR_EntryThreshold_Bi_3t_Uninsured.do" 10000 10000 10000 "N" 1

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.791389   .7129842     2.51   0.012     .3939656    3.188812
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   2.367212    .728063     3.25   0.001     .9402348    3.794189
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   115.2773   169.6688     0.68   0.497    -217.2674     447.822
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    130.037   172.8216     0.75   0.452     -208.687     468.761
------------------------------------------------------------------------------

. nlcom atanh(/r1)

       _nl_1: atanh(/r1)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |   .5077283   .1497614     3.39   0.001     .2142014    .8012551
------------------------------------------------------------------------------

. 
. ml clear

. ml model lf brentry_bioprobit_neg_3t (hosp_s:cat_hosp2 = tot_pop, nocons) (hosp_v:cat_hosp2 = ru
> ral income_pc hispanic nonhisp_black gte_highschool age_65, nocons) (hosp_f:cat_hosp2 = cms_wage
> _index con_intensity, nocons) /hosp_a1 /hosp_g1 (ucc_s:cat_ucc = tot_pop2, nocons) (ucc_v:cat_uc
> c = n_hospitals n_hospaffucc_geo rural income_pc hispanic nonhisp_black gte_highschool age_65, n
> ocons) (ucc_f:cat_ucc = cms_wage_index, nocons) /ucc_a1 /ucc_a2 /ucc_a3 /ucc_g1 /ucc_g2 /ucc_g3 
> (aucc_s:cat_aucc = tot_pop3, nocons) (aucc_v:cat_aucc = n_hospitals n_urgentcare rural income_pc
>  hispanic nonhisp_black gte_highschool age_65, nocons) (aucc_f:cat_aucc = cms_wage_index, nocons
> ) /aucc_a1 /aucc_a2 /aucc_g1 /aucc_g2 /r1 /r2 /r3 if high_uninsured==0, constraints(1 2 3 4 5) t
> echnique(bfgs)

. eststo: ml max, difficult iterate(300) tolerance(1e-4) ltolerance(1e-5)

Initial:      Log likelihood =     -<inf>  (could not be evaluated)
Feasible:     Log likelihood = -1160.1003
Rescale:      Log likelihood = -978.90779
Rescale eq:   Log likelihood = -735.83542
Iteration 0:  Log likelihood = -1160.6709  
Iteration 1:  Log likelihood = -990.85173  (backed up)
Iteration 2:  Log likelihood = -937.58434  (backed up)
Iteration 3:  Log likelihood = -923.33306  (backed up)
Iteration 4:  Log likelihood = -919.44195  (backed up)
Iteration 5:  Log likelihood = -917.90358  (backed up)
Iteration 6:  Log likelihood = -916.66068  (backed up)
Iteration 7:  Log likelihood = -904.01601  
Iteration 8:  Log likelihood = -891.58874  (backed up)
Iteration 9:  Log likelihood = -847.78495  
Iteration 10: Log likelihood = -826.76196  
Iteration 11: Log likelihood = -783.86956  
Iteration 12: Log likelihood = -772.03962  
Iteration 13: Log likelihood = -766.77877  
Iteration 14: Log likelihood = -763.32394  
Iteration 15: Log likelihood = -761.09649  
Iteration 16: Log likelihood = -759.00088  
Iteration 17: Log likelihood = -756.72914  
Iteration 18: Log likelihood = -752.91862  
Iteration 19: Log likelihood = -748.06956  
Iteration 20: Log likelihood = -742.81598  
Iteration 21: Log likelihood = -737.83487  
Iteration 22: Log likelihood =  -736.2171  
Iteration 23: Log likelihood = -735.74358  
Iteration 24: Log likelihood = -734.70508  
Iteration 25: Log likelihood = -733.05968  
Iteration 26: Log likelihood = -731.68574  
Iteration 27: Log likelihood = -729.16138  
Iteration 28: Log likelihood = -716.86756  
Iteration 29: Log likelihood = -699.79036  
Iteration 30: Log likelihood = -694.61133  
Iteration 31: Log likelihood = -693.41193  
Iteration 32: Log likelihood = -692.48314  
Iteration 33: Log likelihood = -688.49064  
Iteration 34: Log likelihood = -684.55907  
Iteration 35: Log likelihood = -682.96289  
Iteration 36: Log likelihood = -682.59902  
Iteration 37: Log likelihood = -682.33889  
Iteration 38: Log likelihood = -681.54777  
Iteration 39: Log likelihood = -678.08199  
Iteration 40: Log likelihood = -677.33405  
Iteration 41: Log likelihood = -677.19681  
Iteration 42: Log likelihood = -676.81898  
Iteration 43: Log likelihood = -675.07025  
Iteration 44: Log likelihood = -673.17211  
Iteration 45: Log likelihood = -672.73674  
Iteration 46: Log likelihood = -672.67886  
Iteration 47: Log likelihood = -672.62401  
Iteration 48: Log likelihood = -672.32596  
Iteration 49: Log likelihood = -669.86796  
Iteration 50: Log likelihood = -668.99258  
Iteration 51: Log likelihood = -668.93698  
Iteration 52: Log likelihood =  -668.8714  
Iteration 53: Log likelihood =  -668.2924  
Iteration 54: Log likelihood = -666.76079  
Iteration 55: Log likelihood = -666.00002  
Iteration 56: Log likelihood = -665.90951  
Iteration 57: Log likelihood = -665.90186  
Iteration 58: Log likelihood = -665.89714  
Iteration 59: Log likelihood =  -665.8491  
Iteration 60: Log likelihood = -665.58072  
Iteration 61: Log likelihood = -665.25732  
Iteration 62: Log likelihood = -665.21547  
Iteration 63: Log likelihood = -665.21273  
Iteration 64: Log likelihood = -665.18526  
Iteration 65: Log likelihood = -664.98111  
Iteration 66: Log likelihood = -664.74675  
Iteration 67: Log likelihood = -664.72831  
Iteration 68: Log likelihood = -664.72758  
Iteration 69: Log likelihood = -664.72619  
Iteration 70: Log likelihood =  -664.7158  
Iteration 71: Log likelihood = -664.44231  
Iteration 72: Log likelihood =  -664.2956  
Iteration 73: Log likelihood = -664.29005  
Iteration 74: Log likelihood = -664.28917  
Iteration 75: Log likelihood =  -664.2379  
Iteration 76: Log likelihood = -663.80348  
Iteration 77: Log likelihood = -663.58952  
Iteration 78: Log likelihood = -663.57945  
Iteration 79: Log likelihood = -663.57882  
Iteration 80: Log likelihood = -663.57269  
Iteration 81: Log likelihood =  -663.5188  
Iteration 82: Log likelihood =   -663.067  
Iteration 83: Log likelihood = -662.75715  
Iteration 84: Log likelihood = -662.74586  
Iteration 85: Log likelihood = -662.74212  
Iteration 86: Log likelihood = -662.65644  
Iteration 87: Log likelihood =  -661.9506  
Iteration 88: Log likelihood =  -661.4928  
Iteration 89: Log likelihood = -661.45291  
Iteration 90: Log likelihood = -661.45089  
Iteration 91: Log likelihood = -661.44954  
Iteration 92: Log likelihood = -661.42933  
Iteration 93: Log likelihood = -661.00134  
Iteration 94: Log likelihood = -660.81892  
Iteration 95: Log likelihood = -660.81124  
Iteration 96: Log likelihood =  -660.8111  
Iteration 97: Log likelihood =  -660.8105  
Iteration 98: Log likelihood = -660.79432  
Iteration 99: Log likelihood =  -660.7159  
Iteration 100: Log likelihood =  -660.7029  
Iteration 101: Log likelihood =  -660.7028  
Iteration 102: Log likelihood = -660.70264  
Iteration 103: Log likelihood = -660.69417  
Iteration 104: Log likelihood = -660.60863  
Iteration 105: Log likelihood = -660.59181  
Iteration 106: Log likelihood = -660.59165  
Iteration 107: Log likelihood = -660.59115  
Iteration 108: Log likelihood = -660.52504  
Iteration 109: Log likelihood = -660.24299  
Iteration 110: Log likelihood = -660.23066  
Iteration 111: Log likelihood = -660.23059  
Iteration 112: Log likelihood = -660.23027  
Iteration 113: Log likelihood = -660.22427  
Iteration 114: Log likelihood = -660.17787  
Iteration 115: Log likelihood = -660.17267  
Iteration 116: Log likelihood = -660.17266  
Iteration 117: Log likelihood = -660.17259  
Iteration 118: Log likelihood = -660.17163  
Iteration 119: Log likelihood = -660.14169  
Iteration 120: Log likelihood = -660.03117  
Iteration 121: Log likelihood = -660.03092  
Iteration 122: Log likelihood = -660.03092  
Iteration 123: Log likelihood = -660.03088  
Iteration 124: Log likelihood = -660.03047  
Iteration 125: Log likelihood =  -660.0256  
Iteration 126: Log likelihood = -659.95653  
Iteration 127: Log likelihood = -659.87596  
Iteration 128: Log likelihood = -659.87221  
Iteration 129: Log likelihood =  -659.8722  
Iteration 130: Log likelihood =  -659.8722  
Iteration 131: Log likelihood = -659.87209  
Iteration 132: Log likelihood = -659.83619  
Iteration 133: Log likelihood =  -659.8248  
Iteration 134: Log likelihood = -659.82463  
Iteration 135: Log likelihood = -659.82462  
Iteration 136: Log likelihood = -659.82462  
Iteration 137: Log likelihood = -659.82453  
Iteration 138: Log likelihood = -659.82091  
Iteration 139: Log likelihood = -659.81535  
Iteration 140: Log likelihood = -659.81471  
Iteration 141: Log likelihood = -659.81471  
Iteration 142: Log likelihood = -659.81471  
Iteration 143: Log likelihood = -659.81448  
Iteration 144: Log likelihood = -659.81167  
Iteration 145: Log likelihood = -659.80307  
Iteration 146: Log likelihood = -659.80041  
Iteration 147: Log likelihood = -659.80035  
Iteration 148: Log likelihood = -659.80035  
Iteration 149: Log likelihood = -659.80035  
Iteration 150: Log likelihood = -659.80026  
Iteration 151: Log likelihood = -659.79771  
Iteration 152: Log likelihood = -659.79727  
Iteration 153: Log likelihood = -659.79727  
Iteration 154: Log likelihood = -659.79727  
Iteration 155: Log likelihood = -659.79727  
Iteration 156: Log likelihood = -659.79704  
Iteration 157: Log likelihood = -659.79687  

                                                           Number of obs = 336
                                                           Wald chi2(0)  =   .
Log likelihood = -659.79687                                Prob > chi2   =   .

 ( 1)  [hosp_s]tot_pop = 1
 ( 2)  [ucc_s]tot_pop2 = 1
 ( 3)  [aucc_s]tot_pop3 = 1
 ( 4)  [/]r1 - [/]r2 = 0
 ( 5)  [/]r2 - [/]r3 = 0
----------------------------------------------------------------------------------
                 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
hosp_s           |
         tot_pop |          1  (constrained)
-----------------+----------------------------------------------------------------
hosp_v           |
           rural |   95.97778   60.98396     1.57   0.116    -23.54858    215.5041
       income_pc |   16.78689   15.14812     1.11   0.268    -12.90288    46.47667
        hispanic |   -228.447   75.75846    -3.02   0.003    -376.9308   -79.96312
   nonhisp_black |  -211.7044   843.3412    -0.25   0.802    -1864.623    1441.214
  gte_highschool |  -537.1256   320.5298    -1.68   0.094    -1165.352    91.10125
          age_65 |    513.845   278.6216     1.84   0.065    -32.24333    1059.933
-----------------+----------------------------------------------------------------
hosp_f           |
  cms_wage_index |   1.080901    .706063     1.53   0.126    -.3029569    2.464759
   con_intensity |   .8176947   .3226039     2.53   0.011     .1854026    1.449987
-----------------+----------------------------------------------------------------
        /hosp_a1 |   255.6468   131.5063     1.94   0.052     -2.10076    513.3944
        /hosp_g1 |    .217651   .6933946     0.31   0.754    -1.141377    1.576679
-----------------+----------------------------------------------------------------
ucc_s            |
        tot_pop2 |          1  (constrained)
-----------------+----------------------------------------------------------------
ucc_v            |
     n_hospitals |  -57.46375   20.49911    -2.80   0.005    -97.64126   -17.28624
n_hospaffucc_geo |  -49.34217   9.743664    -5.06   0.000     -68.4394   -30.24494
           rural |   116.8837   57.18128     2.04   0.041     4.810481     228.957
       income_pc |  -4.171896   14.75412    -0.28   0.777    -33.08944    24.74565
        hispanic |  -158.5088   84.06685    -1.89   0.059    -323.2768    6.259196
   nonhisp_black |  -1020.288   780.0056    -1.31   0.191    -2549.071    508.4951
  gte_highschool |   -209.974   312.0599    -0.67   0.501    -821.6002    401.6522
          age_65 |   18.94294    314.721     0.06   0.952    -597.8989    635.7848
-----------------+----------------------------------------------------------------
ucc_f            |
  cms_wage_index |   .2560508   .5992076     0.43   0.669    -.9183745    1.430476
-----------------+----------------------------------------------------------------
         /ucc_a1 |   590.4867   137.6009     4.29   0.000     320.7939    860.1795
         /ucc_a2 |   185.0315   50.30513     3.68   0.000     86.43527    283.6277
         /ucc_a3 |   10.95264   14.82254     0.74   0.460    -18.09901    40.00428
         /ucc_g1 |   1.311906   .6057061     2.17   0.030     .1247438    2.499068
         /ucc_g2 |   .1375871   .1627245     0.85   0.398    -.1813471    .4565212
         /ucc_g3 |   .5549106   .1447718     3.83   0.000      .271163    .8386582
-----------------+----------------------------------------------------------------
aucc_s           |
        tot_pop3 |          1  (constrained)
-----------------+----------------------------------------------------------------
aucc_v           |
     n_hospitals |  -21.16859   14.23923    -1.49   0.137    -49.07697     6.73978
    n_urgentcare |  -51.68702   12.28476    -4.21   0.000     -75.7647   -27.60934
           rural |   104.6282    47.2059     2.22   0.027     12.10634    197.1501
       income_pc |   .2983814   11.23945     0.03   0.979    -21.73054     22.3273
        hispanic |  -69.09015   53.22419    -1.30   0.194    -173.4076    35.22735
   nonhisp_black |  -11.63175   732.6246    -0.02   0.987     -1447.55    1424.286
  gte_highschool |  -49.13763   241.8437    -0.20   0.839    -523.1427    424.8674
          age_65 |   151.0556   214.3932     0.70   0.481    -269.1473    571.2585
-----------------+----------------------------------------------------------------
aucc_f           |
  cms_wage_index |   .2956003   .5689021     0.52   0.603    -.8194273    1.410628
-----------------+----------------------------------------------------------------
        /aucc_a1 |   235.8326   110.5559     2.13   0.033     19.14696    452.5182
        /aucc_a2 |  -2.931291   6.385303    -0.46   0.646    -15.44626    9.583673
        /aucc_g1 |   1.036775    .560366     1.85   0.064    -.0615222    2.135072
        /aucc_g2 |   .5079141   .0995031     5.10   0.000     .3128916    .7029365
             /r1 |   .4585977   .1151557     3.98   0.000     .2328967    .6842987
             /r2 |   .4585977   .1151557     3.98   0.000     .2328967    .6842987
             /r3 |   .4585977   .1151557     3.98   0.000     .2328967    .6842987
----------------------------------------------------------------------------------
(est6 stored)

. putexcel set "BR_EntryThreshold_Results_V7.xlsx", modify sheet("entry")

. qui do "BR_EntryThreshold_Bi_3t_Uninsured.do" 10000 10000 10000 "O" 0

. lincom _b[/ucc_g1] + _b[/ucc_g2]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.449493   .6000872     2.42   0.016     .2733437    2.625642
------------------------------------------------------------------------------

. lincom _b[/ucc_g1] + _b[/ucc_g2] + _b[/ucc_g3]

 ( 1)  [/]ucc_g1 + [/]ucc_g2 + [/]ucc_g3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   2.004404   .6088002     3.29   0.001     .8111772     3.19763
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   405.4552   129.0615     3.14   0.002     152.4993    658.4111
------------------------------------------------------------------------------

. lincom _b[/ucc_a1] - _b[/ucc_a2] - _b[/ucc_a3]

 ( 1)  [/]ucc_a1 - [/]ucc_a2 - [/]ucc_a3 = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   394.5026   129.9232     3.04   0.002     139.8579    649.1473
------------------------------------------------------------------------------

. nlcom atanh(/r1)

       _nl_1: atanh(/r1)

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |    .495534   .1458243     3.40   0.001     .2097238    .7813443
------------------------------------------------------------------------------

. 
. estout *, drop(tot_pop*) cells(b(fmt(1)) se(fmt(1))) label mlabels("Low income" "High income" "H
> igh SVI" "Low SVI" "High uninsured" "Low uninsured")

--------------------------------------------------------------------------------------------------
                       Low income  High income     High SVI      Low SVI High unins~d Low uninsu~d
                             b/se         b/se         b/se         b/se         b/se         b/se
--------------------------------------------------------------------------------------------------
hosp_v                                                                                            
Rural                        92.3        140.1        127.1         96.0        100.3         96.0
                             66.1         61.2         57.8         72.6         68.5         61.0
Hispanic                    -68.5        -86.6        -32.3       -320.7        -58.4       -228.4
                             64.3         37.7         48.9         98.0         47.5         75.8
Black                       508.9        758.6        423.1       1070.9        498.2       -211.7
                            267.4        750.7        265.1        717.5        256.1        843.3
High school or more         -23.7        -40.7        -63.4       -529.5        -44.1       -537.1
                            302.2        162.2        229.4        346.2        235.7        320.5
Age 65 or more              366.0        278.0        318.0        282.7        157.6        513.8
                            275.2        191.0        201.8        292.4        192.9        278.6
Uninsured                   -11.7         38.4        -76.9        238.2                          
                            261.8        173.7        195.2        275.3                          
Income per capita                                      -5.7         21.1        -15.6         16.8
                                                       13.6         17.6         16.0         15.1
Income per capita                                                                 0.0             
                                                                                    .             
--------------------------------------------------------------------------------------------------
hosp_f                                                                                            
CMS wage index                0.7          1.2          1.2          0.4          0.2          1.1
                              0.9          0.7          0.7          0.9          0.8          0.7
CON laws                      0.5          0.8          0.6          0.8          0.5          0.8
                              0.3          0.3          0.3          0.3          0.4          0.3
--------------------------------------------------------------------------------------------------
/                                                                                                 
hosp_a1                      94.2         64.5        106.7        251.6        142.4        255.6
                            152.7         88.6         98.9        143.3        106.4        131.5
hosp_g1                       0.9         -0.1          0.3          0.9          1.2          0.2
                              0.9          0.7          0.7          0.8          0.8          0.7
--------------------------------------------------------------------------------------------------
ucc_v                                                                                             
Additional hospita~e        -95.9        -43.4        -91.5        -40.4        -67.8        -57.5
                             27.8         20.5         23.6         22.6         26.3         20.5
Number of AUCCs             -82.9        -35.1        -58.7        -42.2        -46.2        -49.3
                             14.5          9.3         13.3         10.6         14.9          9.7
Rural                       146.4          3.8        128.7         28.9          6.9        116.9
                             63.3         59.9         57.4         69.1         69.0         57.2
Hispanic                     43.0       -170.8         14.6       -171.6        -76.5       -158.5
                             80.5         54.5         76.0        126.6         70.3         84.1
Black                      -238.0       -396.7       -196.1       -474.3       -196.8      -1020.3
                            266.1        695.2        263.9        649.3        264.2        780.0
High school or more        1374.2       -475.0        920.6       -113.8        529.0       -210.0
                            491.1        180.4        457.3        348.3        455.0        312.1
Age 65 or more              544.5       -200.7        363.0       -180.5        225.8         18.9
                            304.9        256.7        272.3        330.4        266.6        314.7
Uninsured                   335.5        549.8        255.9        262.9                          
                            286.0        305.4        270.5        370.0                          
Income per capita                                      31.4        -12.1        -20.2         -4.2
                                                       34.5         17.1         37.1         14.8
--------------------------------------------------------------------------------------------------
ucc_f                                                                                             
CMS wage index               -0.4          0.7         -0.3          1.1         -0.0          0.3
                              0.7          0.6          0.6          0.8          0.7          0.6
--------------------------------------------------------------------------------------------------
/                                                                                                 
hosp_a1                      94.2         64.5        106.7        251.6        142.4        255.6
                            152.7         88.6         98.9        143.3        106.4        131.5
hosp_g1                       0.9         -0.1          0.3          0.9          1.2          0.2
                              0.9          0.7          0.7          0.8          0.8          0.7
--------------------------------------------------------------------------------------------------
aucc_v                                                                                            
Additional hospita~e        -73.5        -13.4        -41.6        -21.3        -31.2        -21.2
                             20.1         12.8         14.8         15.3         15.6         14.2
Number of UCCs              -58.4        -49.6        -44.8        -57.7        -51.7        -51.7
                             13.2         12.3         11.9         12.7         13.0         12.3
Rural                       112.3        101.4        137.6         77.1         90.5        104.6
                             58.2         48.3         48.4         57.1         59.5         47.2
Hispanic                    -69.3        -36.1        -75.4       -110.2        -83.9        -69.1
                             49.2         27.6         25.0         76.0         23.0         53.2
Black                      -814.6        381.6       -459.3       -301.6       -517.4        -11.6
                            366.6        567.3        273.8        628.1        283.0        732.6
High school or more         387.4       -100.7        122.2          8.8        119.2        -49.1
                            263.0        118.3        129.4        276.4        133.7        241.8
Age 65 or more              266.8       -230.0        -90.5        118.0       -159.1        151.1
                            211.4        131.5        129.3        231.1        130.7        214.4
Uninsured                   344.3         91.0        218.8        244.0                          
                            169.9        150.0        131.4        218.2                          
Income per capita                                      -4.4         -6.4        -18.8          0.3
                                                       11.1         13.6         12.0         11.2
--------------------------------------------------------------------------------------------------
aucc_f                                                                                            
CMS wage index               -1.4          1.1         -0.7          0.6         -0.4          0.3
                              0.7          0.6          0.5          0.8          0.7          0.6
--------------------------------------------------------------------------------------------------
/                                                                                                 
hosp_a1                      94.2         64.5        106.7        251.6        142.4        255.6
                            152.7         88.6         98.9        143.3        106.4        131.5
hosp_g1                       0.9         -0.1          0.3          0.9          1.2          0.2
                              0.9          0.7          0.7          0.8          0.8          0.7
--------------------------------------------------------------------------------------------------

. 
. log close
      name:  <unnamed>
       log:  V:\Health_IT\Urgent_Care\R2\ucc_replication\BR_EntryThreshold_PCSA_F.log
  log type:  text
 closed on:  23 Aug 2023, 12:54:00
--------------------------------------------------------------------------------------------------
